CN111610262A - Metabolism marker for diagnosing liver and gall diseases - Google Patents
Metabolism marker for diagnosing liver and gall diseases Download PDFInfo
- Publication number
- CN111610262A CN111610262A CN202010423489.9A CN202010423489A CN111610262A CN 111610262 A CN111610262 A CN 111610262A CN 202010423489 A CN202010423489 A CN 202010423489A CN 111610262 A CN111610262 A CN 111610262A
- Authority
- CN
- China
- Prior art keywords
- metabolic
- marker
- serum
- liver
- metabolic marker
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/26—Conditioning of the fluid carrier; Flow patterns
- G01N30/28—Control of physical parameters of the fluid carrier
- G01N30/34—Control of physical parameters of the fluid carrier of fluid composition, e.g. gradient
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Library & Information Science (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention discloses a metabolic marker for diagnosing liver and gall diseases and a screening method thereof, wherein the metabolic marker comprises a serum metabolic marker and a urine metabolic marker, and the serum metabolic marker comprises one or any combination of EPA, AA, DHA, TCDA, DCA, GCA, TCA, GCDA, GCDCA, GDCA, CDCA, LPC16-0, LPC18-0, LPC18-1 and LPC 20-0. The screening method utilizes a pre-established LC-MS/MS method to quantitatively detect the content of target metabolites in large clinical samples (serum and urine) and screen diagnostic markers. The metabolic marker can effectively diagnose liver and gall diseases, reduce the omission rate of liver cancer and bile duct cancer, is very beneficial to early diagnosis and early treatment of the liver cancer and the bile duct cancer, is greatly helpful for improving the prognosis of the liver cancer and the bile duct cancer and reducing the death rate of the liver cancer and the bile duct cancer, and has good clinical use and popularization values. In practical application, more samples and more metabolite combinations can be selected for modeling according to the modeling method, so that the accuracy of the model is improved.
Description
Technical Field
The invention belongs to the field of medical diagnosis, and relates to a metabolic marker for diagnosing liver and gall diseases.
Background
The liver is the most important metabolic organ of human body, not only the most important metabolic site of endogenous metabolites (amino acids, saccharides, lipids, etc.) of human body, but also the most important detoxification organ of millions of exogenous toxicants exposed in the life cycle. Many factors (e.g., high fat, high sugar, alcohol, chemical poisons, hepatitis virus, etc.) can cause damage to the healthy liver, which in turn can progress to hepatitis, cirrhosis and liver cancer. As liver disease progresses, the liver develops different metabolic phenotypes, and the health status of the liver is divided into four levels, stage 0 (Phase 0), according to the difference in metabolic phenotype: healthy liver (health); phase 1 (Phase 1): non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH)/Alcoholic Liver Disease (ALD)/viral hepatitis (viral hepatitis); phase 2 (Phase 2): cirrhosis (cirrhosis); phase 3 (Phase 3): hepatocellular carcinoma (HCC)/cholangiocarcinoma (CCA). Numerous studies have shown that the metabolism of the liver changes significantly during the course of liver disease, and this metabolic phenotypic change mainly involves two aspects, namely: energy metabolic remodeling (beta-oxidation of fatty acids, mitochondrial respiration, and cytosolic glycolysis) and core metabolic phenotype production (decrease in serum lysolecithin, increase in serum and urine bile acid content).
Screening for diagnostic markers is a challenging task. Metabolites (endogenous small molecules with molecular weight less than 1000) are the most downstream levels of life activities (gene → RNA → protein → metabolite), and are the substances closest to biological phenotype, and because of the "cascade amplification" effect of signal transduction, small changes at the gene level can cause large fluctuation of metabolite expression level, so that the metabolites have the natural advantage of high sensitivity as diagnostic markers. However, metabolites as diagnostic markers have their own drawbacks, namely that the specificity is poor, since the tissue specificity of metabolites is not as high as that of proteins, and thus different diseases may cause the same metabolite change. However, the synthesis of lysolecithin and bile acid is basically performed in liver cells, so that the expression levels of the two metabolites are directly affected by liver diseases, and the screening of several substances in the two metabolites as diagnostic markers for the progress of liver diseases should have good sensitivity and specificity.
Metabolites have been used clinically as diagnostic markers for several applications, such as: glucose as a diagnostic marker for diabetes; phenylpyruvic acid is used as a diagnostic marker of phenylketonuria; creatinine and urea as diagnostic markers of renal function; sarcosine is used as a diagnostic marker for prostate cancer, and the like. However, compared with diagnostic markers of proteins (such as tumor markers: carcino-embryonic antigen (CEA), alpha-fetoprotein (AFP), CA19-9, CA125, PSA, etc., liver function markers: ALT, AST, etc., kidney function markers: urine beta 2-microglobulin, urine albumin, urine immunoglobulin G, etc.), the diagnostic markers are much less in general and need to be further developed. Currently, there are two main types of protein markers for clinical diagnosis of liver diseases: (1) liver function markers: ALT, AST; (2) liver cancer markers: AFP. Liver function indices (ALT, AST) reflect the functional status of the liver as the main metabolic detoxification organ, not the pathological level of the liver; the liver cancer marker AFP is mainly used for diagnosis and curative effect evaluation of liver cancer, but has many defects, such as: (1) the sensitivity for diagnosing early liver cancer is poor, the AFP content in serum of many early liver cancer patients is very low (<20 mug/L), and the false negative rate is about 30%; (2) insufficient specificity, pregnancy, embryonic cancers such as testicular cancer, ovarian cancer and few gastric, pancreatic, biliary, colorectal cancers can also rise, and in addition other liver diseases such as hepatitis, cirrhosis, etc. can also lead to elevated AFP, and therefore; serum AFP is used as a diagnostic marker of hepatocellular carcinoma, and the accuracy rate is 60-70%. Based on the change of the core metabolic phenotype of the liver disease, it is necessary and meaningful to screen new markers different from ALT, AST, AFP and other common clinical indicators as supplements.
The rapid development of modern separation detection technology, especially the continuous upgrade of chromatography-mass spectrometry technology and the continuous improvement of big data algorithm, provides possibility for the high-throughput analysis of metabolites. The non-targeted metabonomics (Untargetedmetabolomics) technology is to perform full coverage analysis on metabonomics (endogenous metabolic small molecules with the molecular weight less than 1000) of a biological sample (cells, tissues, body fluid and the like) by using a gas chromatography-mass spectrometry (GC-MS) and/or a liquid chromatography-mass spectrometry (LC-MS), and then screen differentially expressed metabolites among different sample groups by combining a chemometric method, wherein the result shown in figure 1 is that
And the schematic diagram is summarized according to a large number of reported liver disease non-targeted metabonomics research results. However, there is much work to do to develop important differential metabolites as diagnostic markers, mainly including: (1) the metabolites given by the non-targeted metabonomic detection are relatively quantitative results (generally expressed by mass spectrum peak areas), and are comparable to samples detected in the same batch, but are not comparable to samples in different batches, so that in order to develop important differential metabolites into diagnostic markers, a quantitative detection method of the metabolites must be developed by using standard products of the metabolites, so that the absolute concentration information of the metabolites in the samples can be given, and the samples in different batches are comparable; (2) it is necessary to use large clinical samples for verification, which differential metabolites have high sensitivity and specificity (retention) as diagnostic indicators, which differential metabolites have low sensitivity and specificity (rejection), and whether diagnostic markers have good stability and detectability.
Disclosure of Invention
The invention aims to quantitatively detect the content of target metabolites in a large clinical sample (serum and urine) by utilizing a pre-established LC-MS/MS method, screen a diagnostic marker, calculate the Cut off value of the diagnostic marker, evaluate the health level of the liver by utilizing the screened diagnostic marker, track the progress of liver diseases and perform early warning of liver cancer by combining AFP.
The invention provides a metabolic marker for diagnosing liver and gall diseases, which comprises a serum metabolic marker and a urine metabolic marker.
The serum metabolic markers comprise single or any combination of EPA, AA, DHA, TCDA, DCA, GCA, TCA, GCDA, GCDCA, GDCA, CDCA, LPC16-0, LPC18-0, LPC18-1, LPC20-0 and the like; preferably, the serum metabolic marker is a serum marker combination (GCDA + CDCA + LPC18-0+ AA).
The urine metabolism markers comprise single or any combination of LPC16-0, LPC18-0, LPC18-1, AA, GCDA, GCA, GCDCA, TCDA, TCA and the like; preferably, the urine metabolic marker is a urine marker combination (LPC16-0+ TCDA + GCA).
The invention also provides a screening method of the metabolic marker for diagnosing liver and gall diseases, which comprises the following steps:
(1) the content of important target metabolic markers in serum and urine is quantitatively detected based on an LC-MS/MS technology.
(2) And (3) calculating a receiver operating characteristic curve (ROC) based on the logistic model, and screening the optimal metabolic marker combination quantity.
(3) The optimal metabolic marker combinations in serum and urine were screened by wien graph analysis of metabolic marker combinations between different groups.
In the step (1), the number of the serum target metabolic markers is 15, and the serum target metabolic markers are respectively as follows: EPA, AA, DHA, TCDA, DCA, GCA, TCA, GCDA, GCDCA, GDCA, CDCA, LPC16-0, LPC18-0, LPC18-1, LPC 20-0.
In the step (1), the urine target metabolic markers are 9, which are respectively: LPC16-0, LPC18-0, LPC18-1, AA, GCDA, GCA, GCDCA, TCDA, TCA.
In the step (2), R3.6.1 software is preferably adopted to calculate a receiver operating characteristic curve (ROC curve) based on the logistic model.
In step (2), the optimal number of metabolic marker combinations for the serum is 4.
In step (2), the urine has an optimal combined number of metabolic markers of 3.
In the step (3), the serum metabolic marker combination is (GCDA + CDCA + LPC18-0+ AA).
In the step (3), the urine metabolic marker combination is (LPC16-0+ TCDA + GCA).
The invention also provides application of the metabolic marker in diagnosis of liver and gall diseases, treatment effect evaluation of liver and gall diseases and prognosis intervention marker of liver and gall diseases.
The invention also provides application of the metabolic marker as a biomarker in preparation of liver and gall disease diagnosis products and/or liver and gall disease curative effect evaluation products and/or liver and gall disease prognosis intervention products.
The invention also provides application of the metabolic marker and AFP as combined markers in diagnosis of liver and gall diseases, treatment effect evaluation of liver and gall diseases and prognosis intervention markers of liver and gall diseases.
The invention also provides application of the metabolic marker and AFP as combined markers in preparation of liver and gall disease diagnosis products and/or liver and gall disease curative effect evaluation products and/or liver and gall disease prognosis intervention products.
The invention also provides application of the specific detection reagent of the metabolic marker in preparation of liver and gall disease diagnosis products and/or liver and gall disease curative effect evaluation products and/or liver and gall disease prognosis intervention products.
The invention also provides application of the specific combined detection reagent of the metabolic marker and AFP in preparing a hepatobiliary disease diagnosis product and/or a hepatobiliary disease curative effect evaluation product and/or a hepatobiliary disease prognosis intervention product.
The invention also provides a diagnostic product for liver and gall diseases, which comprises a specific detection reagent of the metabolic marker or the combination thereof; or the product comprises a specific combined detection reagent for the metabolic marker and AFP.
The invention also provides a product for evaluating the curative effect of liver and gall diseases, which comprises a specific detection reagent of the metabolic marker or the combination thereof; or the product comprises a specific combined detection reagent for the metabolic marker and AFP.
The invention also provides a liver and gall disease prognosis intervention product, which comprises a specific detection reagent of the metabolic marker or the combination thereof; or the product comprises a specific combined detection reagent for the metabolic marker and AFP.
In the invention, the products related to the liver and gall diseases (liver and gall disease diagnosis products, liver and gall disease curative effect evaluation products and liver and gall disease prognosis intervention products) comprise a kit, test paper and a solid support; the solid support comprises an array, a microarray, or a protein array.
In the invention, the diagnosis product of the hepatobiliary disease is used for detecting the levels of serum metabolic markers and urine metabolic markers in a human body and monitoring the progress of the hepatobiliary disease and the early diagnosis of liver cancer and cholangiocarcinoma.
In the present invention, the diagnostic product for hepatobiliary diseases contains a reagent that specifically recognizes the metabolic markers (including serum metabolic markers, urine metabolic markers, singly or in any combination).
In the present invention, the diagnostic product for liver and gall diseases contains a standard substance or a positive control.
In the invention, the liver and gall diseases comprise hepatitis, cirrhosis, liver cancer, cholangiocarcinoma and the like.
The present invention also provides a diagnostic model for liver and gall disease, the model comprising: (1) determining the concentration of metabolic markers in serum and urine, (2) fitting of multiple markers, (3) discriminant analysis, and (4) visual output of diagnosis results.
(1) Determining the concentration of metabolic markers in serum and urine
Wherein the concentration of the metabolic marker in serum and urine can be, for example, as shown in fig. 3 and 4.
(2) Fitting of multiple markers
The algorithm for fitting the multiple markers provided by the invention specifically comprises the following steps:
logistic regression, also known as logistic regression analysis, is a generalized linear regression analysis model that can be used to determine markers of disease and predict the probability of disease occurrence based on the markers. The values of dependent variables can be defined as morbidity (positive, mortality, cure, etc.) and non-morbidity (negative, survival, no cure, etc.), and independent variables can include a variety of markers. The arguments may be either continuous or categorical. Through logistic regression analysis, the weight of the independent variable can be obtained, and the diagnosis capacity of the multi-index diagnosis marker combination can be judged according to the weight value and a receiver operating characteristic curve (ROC).
The specific algorithm formula for logistic regression analysis is as follows:
conventional term α denotes the argument XjThe natural logarithm of the ratio of the incidence to the non-incidence probability of an individual at 0.
Coefficient of regression βj(j ═ 1,2,. cndot., m) represents the argument XjThis variable of logit (p) is changed by one unit.
Wherein, the receiver operating characteristic curve (ROC) analysis is as follows:
the ROC plot is a curve reflecting the relationship between sensitivity and specificity. The X axis of the abscissa is 1-specific, also called false positive rate (false positive rate), the closer the X axis is to zero, the higher the accuracy rate; the Y-axis on the ordinate is called sensitivity, also called true positive rate (sensitivity), with larger Y-axes representing better accuracy. The whole graph is divided into two parts according to the curve position, the area of the part below the curve is called AUC (area Under Current) and is used for expressing the prediction accuracy, and the higher the AUC value is, namely the larger the area Under the curve is, the higher the prediction accuracy is. The closer the curve is to the upper left corner (the smaller X, the larger Y), the higher the prediction accuracy. The specific algorithm principle is shown in fig. 17 as follows:
wherein, false positive rate (fp rate): the ratio of pairs is originally a wrong prediction (the smaller the better, 0 is an ideal state),
true positive rate (tp rate): the ratio of the original pair to the predicted pair (1 is an ideal state as the larger the better),
precision (precision): of the pairs, the ratio of the pairs is originally predicted (the larger the ratio, the better, 1 is an ideal state),
recall (recall): of the pair originally, the ratio of the pair is predicted (the larger the better, 1 is an ideal state),
f measure (F-measure): making a balance between accuracy and recall (the larger the better, the 1 is in an ideal state, at the moment, precision is 1, and recall is 1);
pair judgment accuracy (accuracy): the ratio of the prediction pairs (including two cases that the prediction is originally a pair and the prediction is originally a mistake) to the whole (the larger the better, the 1 is an ideal state),
predicting TP as positive class number; FP is predicted as a positive class number; the TN ═ negative class is predicted as a negative class number; FN is predicted as a negative class number; predicting N as a negative class number; p-predicted positive number (3) discriminant analysis
The algorithm of discriminant analysis specifically includes:
with training set dataForming a logistic regression model of the comparison group, respectively substituting the training set and the test set data into the logistic regression model to obtain the weight value of each sample, wherein the distribution trend of the weight value is the distribution trend of different groups of samples (see fig. 16), and the calculation formula of diagnostic accuracy (diagnostic accuracy) is as follows:
TP: the diagnostic marker weight threshold value of the training set distinguishes whether the test set has diseases or not, and the positive classes are predicted to be the number of the positive classes;
TN: the diagnostic marker weight threshold value of the training set distinguishes whether the test set has diseases or not, and the negative class is predicted to be the number of the negative classes;
n: the lumped number is tested.
(4) Visual output of diagnostic results
The visual output of the diagnosis result refers to the distribution trend of the discrimination result in fig. 16, and for any test sample, it is only necessary to substitute the concentrations of the diagnostic markers in serum and urine into the logistic regression model to obtain the weight value of each sample, and compare the weight value of the test sample with the diagnostic threshold in the training set, so as to distinguish whether there is a disease.
The invention also provides the application of the diagnosis model of the liver and gall disease in the diagnosis of the liver and gall disease, the curative effect evaluation and the prognosis intervention.
The invention has the beneficial effects that: the metabolic marker combination can effectively diagnose liver and gall diseases, reduce the omission rate of liver cancer and bile duct cancer, is very beneficial to early diagnosis and early treatment of the liver cancer and the bile duct cancer, is greatly helpful for improving the prognosis of the liver cancer and the bile duct cancer and reducing the death rate of the liver cancer and the bile duct cancer, and has good clinical use and popularization values. In practical application, more samples and more metabolite combinations can be selected for modeling according to the modeling method, so that the accuracy of the model is improved.
Drawings
FIG. 1 is an optimized detection map of a target, wherein, a is an ultra performance liquid chromatography tandem mass spectrometry detection map under an anion mode; and the graph B is an ultra performance liquid chromatography tandem mass spectrometry detection graph in a positive ion mode.
FIG. 2 shows the RSD of the target metabolite content in serum and urine QC is less than 15%, wherein, graph A shows the content of 4 lysolecithins and 3 polyunsaturated fatty acids in serum QC samples; panel B shows the content of 8 bile acids in serum QC samples; FIG. C is the content of 3 lysolecithins in urine QC samples; panel D shows the content of 5 bile acids in urine QC samples.
FIG. 3 shows the content of 15 metabolites of interest in different groups of clinical serum samples.
FIG. 4 shows the content of 9 metabolites of interest in different groups of clinical urine samples.
FIG. 5 is a screening flow chart.
FIG. 6 is a graph showing the ranking of the areas under the ROC curve for different amounts of diagnostic composition when performing a differential analysis using diagnostic indicators for serum.
FIG. 7 is a Wien diagram of diagnostic compositions for normal and different disease groups of sera.
FIG. 8 is a Wien diagram of diagnostic compositions for different disease groups of sera.
Figure 9 is a wien plot of 8 control diagnostic compositions of serum.
FIG. 10 is a plot of the area under the ROC curve for different amounts of diagnostic composition when using a diagnostic index for urine for a discriminant analysis.
FIG. 11 is a Wien chart of diagnostic compositions for normal and different disease groups of urine.
FIG. 12 is a Wien diagram of diagnostic compositions for different disease groups of urine.
Figure 13 is a wien plot of 8 comparative diagnostic compositions of urine.
FIG. 14 is a comparison of the diagnostic ability of the serum marker combination, AFP and the combination of the two between the healthy and diseased groups.
FIG. 15 is a comparison of the diagnostic capabilities of the serum marker combination, AFP and combination of the two between disease groups.
FIG. 16 is a diagnostic accuracy study.
FIG. 17 is a schematic diagram of ROC analysis;
predicting TP as positive class number; FP is predicted as a positive class number; the TN ═ negative class is predicted as a negative class number; FN is predicted as a negative class number; predicting N as a negative class number; p is predicted as a positive number.
Detailed Description
The present invention will be described in further detail with reference to the following specific examples, and the procedures, conditions, reagents, experimental methods and the like for carrying out the present invention are general knowledge and common general knowledge in the art except for those specifically mentioned below, and the present invention is not particularly limited thereto.
Example 1: screening of diagnostic markers for liver and gallbladder diseases
1. Study object
Clinical serum and urine samples for this study were obtained from 3 independent medical centers, and the specific sample information is shown in table 1.
TABLE 1 clinical serum and urine sample information
2. Ultra high performance liquid chromatography tandem mass spectrometry of target metabolites in serum and urine samples
2.1 information on the target metabolites
15 important differential metabolites were selected from the core metabolic phenotype of liver disease as target metabolites for this screening, detailed information is shown in table 2.
TABLE 2 target metabolites and internal standard information
*Use of labeled metabolites as internal standards
2.2 instruments and reagents
The ultra-high performance liquid chromatograph is connected with a triple quadrupole in series, and the type of a chromatographic system is as follows: ACQUITYUPLC, Waters corporation; model number of mass spectrometry system (AB5500, AB Sciex Co.). Vortex oscillators (TYXH-I, Shanghai Hano instruments, Inc.); desk type high speed refrigerated centrifuge (TGL-16MS, Shanghai Luxiang instrument centrifuge Co., Ltd.)
Methanol, water and acetonitrile are all mass spectrometric purity (Shanghai' an spectrum experiment science and technology Co., Ltd.)
Chloroform for analytical reagent (national medicine group)
Standard (Sigma-Aldrich Co.)
2.3 sample Pre-treatment
To 10 μ L of serum was added 90 μ L of purified water, 300 μ L of protein precipitant (methanol: acetonitrile 2:1, v/v) (containing 1.6 μ g/mL LPC17-0 and 16ng/mL GCA-C13). Vortex for 1 min, standing at-20 deg.C for 20 min, centrifuging at 4 deg.C for 10 min, loading 150 μ L of supernatant into LC-MS sample vial, standing at-80 deg.C, and testing.
mu.L of urine was added to 150. mu.L of protein precipitant (methanol: acetonitrile 2:1, v/v) (containing 64ng/mL LPC17-0 and 16ng/mL GCA-C13), vortexed for 1 minute, allowed to stand at-20 ℃ for 20 minutes, centrifuged at 4 ℃ for 10 minutes, 150. mu.L of supernatant was taken and placed in an LC-MS sample vial, and allowed to stand at-80 ℃ for assay.
The quality control sample (QC) of the serum is formed by mixing serum samples, and the quality control sample of the urine sample is formed by mixing urine samples. The pretreatment method of the quality control sample is completely the same as the pretreatment method of the detection sample of the corresponding type.
2.4 sample testing
All processed serum and urine samples are used as analysis samples, samples of the same type are loaded in the machine in the same batch, and random sequencing is carried out for sample injection after the sequence is disturbed, so as to eliminate the difference between groups caused by the sample injection sequence. One quality control sample was added every 50 analytical samples.
The assay conditions were as follows:
chromatographic conditions of column BEH C18 (2.1 × 50 mm)21.7 μm, Waters Corp.)
Mobile phase: phase a, water (containing 0.1% formic acid); phase B, acetonitrile (containing 0.1% formic acid)
Chromatographic gradient: 10-10% of B, 0-0.2 min; 10-55% of B, 0.2-3.5 minutes; 55-80% of B, 3.5-6 minutes; 80-100% B, 6-6.5 min; 100% B, 6.5-8 min, 100-10% B, 8-8.3 min; 10-10% of B, 8.3-10 minutes.
The flow rate is 0.6 mL/min; column temperature: 45 ℃; sample introduction amount: 3 μ L
Mass spectrum conditions: electrospray ion source, switching between positive and negative ion modes. Ion source temperature: 120 ℃; the flow rate of the carrier gas in the taper hole is as follows: 40L/h; desolventizing temperature: 400 ℃; flow rate of carrier gas: 650L/h; the capillary voltages of the positive and negative ions are respectively: +5.5kV and-5.5 kV; the inlet potential and the collision potential are both 10V, the de-clustering voltage and the collision energy need to be optimized and selected for a single standard product, see table 3 in detail, the optimized detection diagram of the standard product is shown in figure 1, polyunsaturated fatty acid and bile acid are detected in a negative ion mode, lysolecithin is detected in a positive ion mode, the separation degree and mass spectrum response of an object to be detected are good under the current chromatographic mass spectrum condition, and the subsequent detection and analysis of formal samples can be carried out.
2.5 quantification of target metabolites in serum and urine
According to the pretreatment method and the analysis and detection conditions, the content of the target metabolites in all clinical samples is detected, the stability of the test process and the reliability of data are evaluated by using the Relative Standard Deviation (RSD) value of the content of the target metabolites in the quality control sample (QC), and the result shows that the RSD of the content of the target metabolites in serum and urine QC is less than 15 percent (shown in figure 2), which indicates that the whole analysis process is stable and reliable. The content of the target metabolites in the samples of the different groups is shown in the box diagrams, see fig. 3 and fig. 4. During the progression of liver disease (N → HS → CH → HC/CC), the tendency of the contents of lysolecithin and polyunsaturated fatty acids in the serum is the same, both decreasing and then gradually stabilizing; the content of serum bile acids (CDCA, GCDA, GCDCA, GDCA, TCDA) shows a tendency of rising first and then falling during the progression of liver disease (N → HS → CH → HC/CC), and reaches a peak in the cirrhosis group (CH); the tendency of serum bile acid GCA and TCA is consistent, and the liver disease progresses in a double-peak trend of rising first, then falling and then rising, wherein the content of CH group and CC group is higher than that of other groups. The content of serum bile acid DCA in HS, CH and HC is not greatly different and is slightly lower than that in the healthy group (N), and the content in the CC group is the lowest. During the progression of liver disease (N → HS → CH → HC/CC), the tendency of the contents of urine lysolecithin and polyunsaturated fatty acid AA is the same, both of which are a gradually rising tendency; urine bile acid basically shows a trend of ascending first and then descending, wherein the urine bile acid content of the hepatitis group (HS) is the highest.
TABLE 3 optimized parent and child ion pairs and associated mass spectrometric detection parameters
3. Screening for diagnostic markers
3.1 screening protocol
The screening flow chart is as shown in figure 5, firstly, the content of 15 important target metabolites in serum and urine samples of 5 types of clinical samples (N: healthy people, HS: hepatitis, CH: cirrhosis, HC: liver cancer, CC: cholangiocarcinoma) from 2 independent medical centers is quantitatively detected based on LC-MS/MS technology, R3.6.1 software is adopted to calculate a receiver operating characteristic curve (ROC curve) based on a logistic model, and the optimal metabolite combination quantity is screened. The best combination of diagnostic markers in serum and urine was screened by wien graph analysis of the combination of diagnostic markers between the different groups. The diagnostic power of this optimal diagnostic marker combination is compared with the clinically usual diagnostic index AFP, and then another batch of clinical samples (serum of healthy people, liver cancer patients and bile duct cancer patients) is used for diagnostic accuracy test of the diagnostic marker combination.
3.2 screening results
The diagnostic marker can be a single metabolite or a metabolite group consisting of a plurality of metabolites, in this case, R3.6.1 software is used to calculate a receiver operating characteristic curve (ROC curve) based on a logistic model, and the area under the ROC curve (AUC) of all combinations of 15 metabolites is obtained. Plotting the number of metabolite combinations of the serum as abscissa and the AUC value corresponding to the number of metabolite combinations of the serum as ordinate (see fig. 6), the optimal number of metabolite combinations of the serum can be found, namely: model accuracy did not increase any more when the number of metabolites in the combination was increased. The results show that: the combination of 4 metabolites is optimal. In order to screen for the optimal diagnostic combination that distinguishes different clinical specimen groupings, the present invention will find the intersection of the diagnostic compositions of the different groupings. The wien pattern of the diagnostic composition for the normal group and the different disease groups is shown in fig. 7(AUC >0.95, metabolite number 4); the wien plots of the diagnostic compositions for the different disease groups are shown in fig. 8(AUC >0.75, metabolite number 4); the wien plots of the diagnostic compositions of the 8 comparison groups (AUC >0.95 in the normal group vs. disease group, AUC >0.75 in the disease group vs. disease group, and metabolite number 4) are shown in fig. 9. There were 2 intersections of the diagnostic compositions of the 8 control groups, and the AUC values of these 2 combinations in the different control groups are shown in table 4. Diagnostic marker combination 1(GCDA + CDCA + LPC18-0+ AA) was slightly superior to diagnostic marker combination 2(TCDA + CDCA + LPC18-0+ AA).
The screening method of the diagnostic marker group of urine is the same as the screening method of the serum sample, and the optimal metabolite combination amount of urine can be found to be 3 by plotting the metabolite combination amount of urine as the abscissa and the AUC value corresponding to the metabolite combination amount of urine as the ordinate (see fig. 10). Wien plots of the diagnostic compositions for the normal and different disease groups are shown in fig. 11(AUC >0.9, metabolite number 3); the wien plots of the diagnostic compositions for the different disease groups are shown in fig. 12(AUC >0.7, metabolite number 3); the wien plots of the diagnostic compositions of the 8 comparison groups (AUC >0.95 in the normal group vs. disease group, AUC >0.75 in the disease group vs. disease group, and metabolite number ═ 3) are shown in fig. 13. There were 3 intersections of 8 control diagnostic compositions in urine, and the AUC values of these 3 combinations in the different controls are shown in table 5. In summary, the optimal diagnostic marker panel in urine was LPC16-0+ TCDA + GCA.
TABLE 4 AUC values of the two compositions in different control groups of serum samples
TABLE 5 AUC values of three compositions in different control groups of urine samples
3.3 comparison of diagnostic Standard combinations with AFP diagnostic Capacity
AFP is the most common marker for clinical diagnosis of liver cancer at present, and by ROC analysis (see the attached figures 14 and 15) and comparison of AUC values (see the table 6), the invention can find out the diagnostic marker groups screened out from all discriminant analysis groups: AUC values of (GCDA + CDCA + LPC18-0+ AA) were all greater than AUC values of AFP, indicating that diagnostic compositions (GCDA + CDCA + LPC18-0+ AA) had better diagnostic capabilities than AFP. The diagnostic advantage of (GCDA + CDCA + LPC18-0+ AA) was more pronounced in samples with AFP < 20. mu.g/L. In samples with AFP more than or equal to 20 mug/L, the AUC value of AFP is larger than that of the diagnosis composition (GCDA + CDCA + LPC18-0+ AA) when the liver cancer group (HC) and other groups of diseases are subjected to discriminant analysis, and the AUC value of the diagnosis composition (GCDA + CDCA + LPC18-0+ AA) is larger than that of AFP in the discriminant analysis of other groups of diseases except the HC group. The AFP combined diagnostic marker group can not obviously increase the diagnostic capability in all samples or in samples with AFP less than 20 mug/L, and can be independently used for diagnosis without being combined with AFP, but can obviously increase the discriminant analysis capability among different disease groups in the samples with AFP more than or equal to 20 mug/L.
TABLE 6 comparison of diagnostic Capacity of diagnostic marker set with AFP
Example 2: construction of liver and gall cancer diagnosis model and diagnosis accuracy test by using 4 serum metabolic markers
The study subjects of this example were 60 liver cancer and 60 bile duct cancer serum samples from 3 independent medical centers and 60 healthy control serum samples that were examined normally, and were from the same source as the feature screening samples. The detection analysis method of the embodiment 2 of the present invention is the same as that of the embodiment 1 of the present invention. The diagnosis characteristics are 4 serum metabolism combinations (GCDA + CDCA + LPC18-0+ AA), 754 samples (healthy 340 samples, liver cancer 220 samples and bile duct cancer 194 samples) in the embodiment 1 of the invention are used as training sets, 180 samples in the embodiment 2 of the invention are used as test sets, the diagnosis accuracy of the screened serum diagnosis marker combinations (GCDA + CDCA + LPC18-0+ AA) on liver cancer and bile duct cancer is examined, the results are shown in figure 16 and table 7, the serum diagnosis marker combinations (GCDA + CDCA + LPC18-0+ AA) have good discriminant analysis capability on a healthy group and a liver cancer group, a healthy group and a bile duct cancer group, the diagnosis accuracy respectively reaches 91.7% and 86.7%, and is far higher than the accuracy (60-70%) of AFP as a liver cancer diagnosis marker. Therefore, the metabolic marker combination can effectively diagnose liver and gall diseases, reduce the omission rate of liver cancer and bile duct cancer, is very beneficial to early diagnosis and early treatment of the liver cancer and the bile duct cancer, is greatly helpful for improving the prognosis of the liver cancer and the bile duct cancer and reducing the death rate of the liver cancer and the bile duct cancer, and has good clinical use and popularization values. In practical application, more samples and more metabolite combinations can be selected for modeling according to the modeling method, so that the accuracy of the model is improved.
TABLE 7 diagnostic accuracy of hepatocarcinoma and cholangiocarcinoma Using the serum diagnostic marker combination (GCDA + CDCA + LPC18-0+ AA)
The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that other embodiments based on the inventive idea would also fall within the scope of the claims of the present invention for a person with ordinary skill in the art without departing from the principle of the present invention.
Claims (12)
1. A metabolic marker for diagnosis of liver and gall diseases, wherein the metabolic marker comprises a serum metabolic marker, a urine metabolic marker;
wherein the serum metabolic markers comprise EPA, AA, DHA, TCDA, DCA, GCA, TCA, GCDA, GCDCA, GDCA, CDCA, LPC16-0, LPC18-0, LPC18-1, LPC20-0, singly or in any combination; and/or, the urine metabolism marker comprises single or any combination of LPC16-0, LPC18-0, LPC18-1, AA, GCDA, GCA, GCDCA, TCDA and TCA.
2. The metabolic marker of claim 1, wherein the serum metabolic marker comprises GCDA, CDCA, LPC18-0 and AA; and/or, the urinary metabolic markers comprise LPC16-0, TCDA and GCA.
3. A method for screening a metabolic marker for diagnosing liver and gall diseases is characterized by comprising the following steps:
(1) quantitatively detecting the content of metabolic markers in serum and urine based on an LC-MS/MS technology;
(2) calculating a test subject working characteristic curve based on a logistic model, and screening the optimal metabolic marker combination quantity;
(3) the optimal metabolic marker combinations in serum and urine were screened by wien graph analysis of metabolic marker combinations between different groups.
4. The method of claim 3, wherein in step (1), the serum target metabolic markers comprise: EPA, AA, DHA, TCDA, DCA, GCA, TCA, GCDA, GCDCA, GDCA, CDCA, LPC16-0, LPC18-0, LPC18-1, LPC20-0, singly or in any combination; and/or, the urine target metabolic marker comprises: LPC16-0, LPC18-0, LPC18-1, AA, GCDA, GCA, GCDCA, TCDA, TCA, singly or in any combination.
5. The method of claim 4, wherein in step (2), the optimal combination of metabolic markers for the serum is GCDA, CDCA, LPC18-0 and AA; and/or the urine metabolic marker combination is LPC16-0, TCDA and GCA.
6. Use of a metabolic marker as defined in claim 1 or 2 as a biomarker for the preparation of a diagnostic product for a hepatobiliary disease and/or a product for the assessment of the efficacy of a hepatobiliary disease and/or a product for the prognostic intervention of a hepatobiliary disease.
7. Use of a metabolic marker according to claim 1 or 2 and AFP as a combined marker for the preparation of a diagnostic product for hepatobiliary diseases and/or a product for the assessment of the efficacy of hepatobiliary diseases and/or a product for the prognostic intervention of hepatobiliary diseases.
8. Use of a reagent for the specific detection of a metabolic marker according to claim 1 or 2, or a reagent for the specific combined detection of a metabolic marker and AFP according to claim 1 or 2, for the preparation of a diagnostic product for hepatobiliary diseases and/or a product for the assessment of the efficacy of hepatobiliary diseases and/or a product for the prognostic intervention of hepatobiliary diseases.
9. A hepatobiliary disease-related product comprising a detection reagent specific for a metabolic marker according to claim 1 or 2; or, said product comprising a metabolic marker according to claim 1 or 2 and a specific combined detection reagent for AFP.
10. A model for diagnosing liver and gall diseases is characterized by comprising (1) determining the concentration of metabolic markers in serum and urine, (2) fitting of multiple markers, (3) discriminant analysis and (4) visual output of a diagnosis result.
11. The diagnostic model of hepatobiliary disease as set forth in claim 10, wherein the algorithm of step (2) of multi-marker fitting specifically refers to:
obtaining the weight of an independent variable through logistic regression analysis, and judging the diagnosis capacity of the multi-index diagnosis marker combination according to the weight value and a receiver working characteristic curve (ROC);
the specific algorithm formula for logistic regression analysis is as follows:
conventional term α denotes the argument XjThe natural logarithm of the ratio of the individual incidence to non-incidence probability at 0;
coefficient of regression βj(j ═ 1,2,. cndot., m) represents the argument XjThe variable of logit (p) in one unit is changed;
the algorithm of the discriminant analysis in the step (3) specifically comprises the following steps:
forming a logistic regression model of a comparison group by using training set data, respectively substituting the training set data and test set data into the logistic regression model to obtain a weight value of each sample, wherein the distribution trend of the weight values is the distribution trend of different groups of samples, and the calculation formula of the diagnosis accuracy is as follows:
TP: the diagnostic marker weight threshold value of the training set distinguishes whether the test set has diseases or not, and the positive classes are predicted to be the number of the positive classes;
TN: the diagnostic marker weight threshold value of the training set distinguishes whether the test set has diseases or not, and the negative class is predicted to be the number of the negative classes;
n: the lumped number is tested.
12. Use of a hepatobiliary disease-related product according to claim 9, or of a diagnostic model of hepatobiliary disease according to claim 10 or 11, for diagnosis of hepatobiliary disease and/or for efficacy assessment of hepatobiliary disease and/or for prognostic intervention of hepatobiliary disease.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010423489.9A CN111610262A (en) | 2020-05-19 | 2020-05-19 | Metabolism marker for diagnosing liver and gall diseases |
| CN202110458372.9A CN113156018B (en) | 2020-05-19 | 2021-04-27 | Method for establishing liver and gall disease diagnosis model and diagnosis system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010423489.9A CN111610262A (en) | 2020-05-19 | 2020-05-19 | Metabolism marker for diagnosing liver and gall diseases |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111610262A true CN111610262A (en) | 2020-09-01 |
Family
ID=72204840
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010423489.9A Pending CN111610262A (en) | 2020-05-19 | 2020-05-19 | Metabolism marker for diagnosing liver and gall diseases |
| CN202110458372.9A Active CN113156018B (en) | 2020-05-19 | 2021-04-27 | Method for establishing liver and gall disease diagnosis model and diagnosis system |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110458372.9A Active CN113156018B (en) | 2020-05-19 | 2021-04-27 | Method for establishing liver and gall disease diagnosis model and diagnosis system |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN111610262A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112666287A (en) * | 2020-12-22 | 2021-04-16 | 上海鹿明生物科技有限公司 | GCMSMS-based high-throughput analysis method and application of animal intestinal flora metabolites |
| CN112669958A (en) * | 2020-12-08 | 2021-04-16 | 河北医科大学第二医院 | Metabolites as biomarkers for disease diagnosis |
| CN112951332A (en) * | 2021-02-25 | 2021-06-11 | 北京博富瑞基因诊断技术有限公司 | Method for severe intestinal tract aGVHD model based on aGVHD biomarker |
| CN114894926A (en) * | 2022-04-27 | 2022-08-12 | 天津国科医工科技发展有限公司 | Bile acid detection method based on dry blood paper sheet method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050152908A1 (en) * | 2003-11-03 | 2005-07-14 | Genenews Inc. | Liver cancer biomarkers |
| CN101003831A (en) * | 2007-01-17 | 2007-07-25 | 王贤理 | Kit for diagnosing diseases in system of liver and gall |
| CN102445512A (en) * | 2010-10-09 | 2012-05-09 | 中国人民解放军第二军医大学 | Small molecule metabolite map for identifying liver cancer, hepatitis or liver cirrhosis and manufacturing method thereof |
| CN107037144A (en) * | 2016-11-22 | 2017-08-11 | 苏长青 | A kind of detection method of ultra performance liquid chromatography tandem mass spectrum |
| CN109060977A (en) * | 2018-07-13 | 2018-12-21 | 深圳市绘云生物科技有限公司 | Biomarker and kit and application method for liver fibrosis and liver cirrhosis diagnosis |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104812914B (en) * | 2012-09-21 | 2020-07-24 | 新加坡保健集团 | Method for diagnosing liver cancer in subject and kit for diagnosing liver cancer |
| CN105738626B (en) * | 2014-12-12 | 2017-07-14 | 中国科学院大连化学物理研究所 | A kind of combination of new serum protein moteblites and its it is used as the purposes of diagnosing cancer of liver mark |
| CN107945878B (en) * | 2017-11-16 | 2020-11-27 | 祁小龙 | Method for constructing hepatic vein pressure gradient calculation model based on radiology group |
| CN109239210B (en) * | 2018-09-10 | 2019-11-19 | 哈尔滨工业大学 | A kind of ductal adenocarcinoma of pancreas marker and its screening technique |
| CN110577998A (en) * | 2019-01-31 | 2019-12-17 | 上海交通大学医学院附属仁济医院 | Construction of molecular model for predicting postoperative early recurrence risk of liver cancer and application evaluation thereof |
| CN109852714B (en) * | 2019-03-07 | 2020-06-16 | 南京世和基因生物技术有限公司 | Early diagnosis of intestinal cancer and adenoma diagnosis marker and application |
| CN109884302B (en) * | 2019-03-14 | 2023-02-03 | 北京博远精准医疗科技有限公司 | Lung cancer early diagnosis marker based on metabonomics and artificial intelligence technology and application thereof |
| CN110646554B (en) * | 2019-09-12 | 2022-05-13 | 北京博远精准医疗科技有限公司 | Pancreatic cancer diagnosis marker based on metabonomics and screening method and application thereof |
| CN110656168B (en) * | 2019-09-30 | 2023-04-18 | 中南大学 | COPD early diagnosis marker and application thereof |
-
2020
- 2020-05-19 CN CN202010423489.9A patent/CN111610262A/en active Pending
-
2021
- 2021-04-27 CN CN202110458372.9A patent/CN113156018B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050152908A1 (en) * | 2003-11-03 | 2005-07-14 | Genenews Inc. | Liver cancer biomarkers |
| CN101003831A (en) * | 2007-01-17 | 2007-07-25 | 王贤理 | Kit for diagnosing diseases in system of liver and gall |
| CN102445512A (en) * | 2010-10-09 | 2012-05-09 | 中国人民解放军第二军医大学 | Small molecule metabolite map for identifying liver cancer, hepatitis or liver cirrhosis and manufacturing method thereof |
| CN107037144A (en) * | 2016-11-22 | 2017-08-11 | 苏长青 | A kind of detection method of ultra performance liquid chromatography tandem mass spectrum |
| CN109060977A (en) * | 2018-07-13 | 2018-12-21 | 深圳市绘云生物科技有限公司 | Biomarker and kit and application method for liver fibrosis and liver cirrhosis diagnosis |
Non-Patent Citations (1)
| Title |
|---|
| 曹成红: "血清AFP、ANA及LN联合检测在自身免疫性肝炎诊断中的应用", 《标记免疫分析与临床》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112669958A (en) * | 2020-12-08 | 2021-04-16 | 河北医科大学第二医院 | Metabolites as biomarkers for disease diagnosis |
| CN112666287A (en) * | 2020-12-22 | 2021-04-16 | 上海鹿明生物科技有限公司 | GCMSMS-based high-throughput analysis method and application of animal intestinal flora metabolites |
| CN112666287B (en) * | 2020-12-22 | 2022-02-15 | 上海鹿明生物科技有限公司 | GCMSMS-based high-throughput analysis method and application of animal intestinal flora metabolites |
| CN112951332A (en) * | 2021-02-25 | 2021-06-11 | 北京博富瑞基因诊断技术有限公司 | Method for severe intestinal tract aGVHD model based on aGVHD biomarker |
| CN114894926A (en) * | 2022-04-27 | 2022-08-12 | 天津国科医工科技发展有限公司 | Bile acid detection method based on dry blood paper sheet method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113156018B (en) | 2023-04-07 |
| CN113156018A (en) | 2021-07-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113156018B (en) | Method for establishing liver and gall disease diagnosis model and diagnosis system | |
| Kenny et al. | Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning | |
| EP3273248B1 (en) | Method and use of metabolic compounds for diagnosing stroke | |
| CN108414660B (en) | Application of group of plasma metabolism small molecule markers related to early diagnosis of lung cancer | |
| Haoula et al. | Lipidomic analysis of plasma samples from women with polycystic ovary syndrome | |
| AU2010277664A1 (en) | Method for predicting the likelihood of an onset of an inflammation associated organ failure | |
| WO2011157655A1 (en) | Use of bile acids for prediction of an onset of sepsis | |
| Wang et al. | The urinary sarcosine/creatinine ratio is a potential diagnostic and prognostic marker in prostate cancer | |
| CN113406226B (en) | Method for detecting imatinib metabolite in plasma of GIST patient based on non-targeted metabonomics | |
| Jain et al. | Hemoglobin normalization outperforms other methods for standardizing dried blood spot metabolomics: A comparative study | |
| CN113567585A (en) | Esophageal squamous carcinoma screening marker and kit based on peripheral blood | |
| Lin et al. | Metabolomic biomarkers for the diagnosis and post-transplant outcomes of AFP negative hepatocellular carcinoma | |
| Neumann et al. | Sensitive determination of ethyl glucuronide in serum and whole blood: detection time after alcohol exposure compared with urine | |
| Zhang et al. | Preliminary study of urine metabolism in type two diabetic patients based on GC-MS | |
| CN115684451A (en) | Esophageal squamous carcinoma lymph node metastasis diagnosis marker based on metabonomics and application thereof | |
| Chen et al. | HPLC for simultaneous quantification of free mannose and glucose concentrations in serum: use in detection of ovarian cancer | |
| EP2339352A1 (en) | Use of endogenous metabolites for early diagnosing sepsis | |
| CN115440375A (en) | Colorectal cancer prediction system and application thereof | |
| CN114755422A (en) | Biomarker for colorectal cancer detection and application thereof | |
| Li et al. | Relationship between amniotic fluid metabolic profile with fetal gender, maternal age, and gestational week | |
| CN111638324B (en) | Coronary heart disease diagnosis biomarker composition and application thereof | |
| Vialaret et al. | Use of dried blood spots for monitoring inflammatory and nutritional biomarkers in the elderly | |
| CN117388495B (en) | Application of metabolic marker for diagnosing lung cancer stage and kit | |
| CN117110627B (en) | Markers for neonatal dry blood spot congenital heart disease and subtype assessment thereof | |
| Cascant-Vilaplana et al. | Longitudinal perturbations of plasma nuclear magnetic resonance profiles in neonatal encephalopathy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200901 |
|
| WD01 | Invention patent application deemed withdrawn after publication |