阅读说明：本技术 一种用于肝胆疾病诊断的代谢标志物 (Metabolism marker for diagnosing liver and gall diseases ) 是由 宁慧 张志永 彭章晓 胡哲 陆嘉伟 胡绪俊 付艳蕾 舒烈波 于 2020-05-19 设计创作，主要内容包括：本发明公开了一种用于肝胆疾病诊断的代谢标志物及其筛选方法,所述代谢标志物包括血清代谢标志物、尿液代谢标志物,所述血清代谢标志物包括EPA,AA,DHA,TCDA,DCA,GCA,TCA,GCDA,GCDCA,GDCA,CDCA,LPC16-0,LPC18-0,LPC18-1,LPC20-0中的单个或任意组合。所述筛选方法利用预先建立好的LC-MS/MS方法,定量检测临床大样本(血清和尿液)中目标代谢物的含量,筛选诊断标志物。本发明的代谢标志物可以有效地诊断出肝胆疾病,降低肝癌和胆管癌漏检率,非常有利于肝癌和胆管癌的早诊早治,对于改善肝癌和胆管癌预后,降低肝癌和胆管癌的死亡率有很大帮助,具有良好的临床使用和推广价值。在实际应用中,可以按照本发明建模方法选取更多的样本和更多的代谢物组合进行建模,增加模型的准确度。(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.)

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 X_jThe natural logarithm of the ratio of the individual incidence to non-incidence probability at 0;

coefficient of regression β_j(j ═ 1,2,. cndot., m) represents the argument X_jThe 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.

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 thatAnd 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 X_jThe natural logarithm of the ratio of the incidence to the non-incidence probability of an individual at 0.

Coefficient of regression β_j(j ═ 1,2,. cndot., m) represents the argument X_jThis 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.