KR20090080318A - GGH, the markers for diagnosing hepatocellular carcinoma, a kit comprising the same and method for predicting hepatocellular carcinoma using the marker - Google Patents

Abstract

A method for predicting hepatocellular carcinoma using GGH marker for hapatocellular carcinoma is provided to analyze expression pattern of a gene related to hepatocellular carcinoma and easily and accurately judge hepatocellular carcinoma. A composition for diagnosing hepatocellular carcinoma comprises an agent which measures the expression level of GGH(Gamma-glutamyl hydrolase). A kit for detecting a marker to diagnose hepatocellular carcinoma comprises the composition. A microarray for diagnosing hepatocelluar carcinoma comprises GGH. A method for detecting to predict prognosis after hepatectomy comprises a step of comparing the expression level of GGH between normal cell and a cell of patient who got the hepatectomy.

Description

GH, the markers for diagnosing hepatocellular carcinoma, a kit comprising the same and method for predicting hepatocellular carcinoma using the marker}

The present invention includes a marker detecting composition for diagnosing liver cancer, comprising a preparation for measuring the expression level of gamma-glutamyl hydrolase (GGH), a kit including the composition, and a microarray for diagnosing liver cancer using the marker. And a method for detecting the marker.

Hepatocellular carcinoma is one of the most deadly cancers in the world, with more than half a million people dying from liver cancer each year, especially in Asia and sub-Saharan Africa. Although the risk factor for liver cancer is known to be a chronic infection caused by hepatitis B or hepatitis C virus, the molecular mechanisms within the liver cancer cells are not clearly identified, and thus, diagnostic markers have been developed that have excellent effects in the diagnosis of liver cancer. It is not true. In addition, liver resection is a treatment for relatively early patients without metastases among liver cancer patients. Currently, about 20% of all liver cancer patients are treated by liver resection. Is not high, especially many patients die within one year after surgery. In addition, although survival prediction by clinical pathology has been used for a long time, it is not applicable to all liver cancer patients due to problems of universality and validity. Therefore, there is a great need to develop effective liver cancer diagnostic markers and hepatic resection prognostic markers.

 Previous studies on the molecular mechanisms of liver cancer have focused on the study of individual genes, and recent studies have shown that p53, beta-catenin, AXINI, and p21 (WAF1 / CIP1) have been altered in the development of liver cancer. And p27 Kip et al. Were found to be involved. However, these individual gene changes do not accurately reflect the external and clinical characteristics of liver cancer patients, and the diversity of cells and molecules within individual liver cancers requires new approaches in addition to conventional genetic studies.

In this regard, cDNA microarray technology has been applied to almost all cancer studies, including liver cancer, as a new technology that can simultaneously measure tens of thousands of gene expressions in one experiment beyond the range of individual genes. By measuring genes actively involved in development and measuring proteins or enzymes expressed in those genes, the level of cancer diagnosis is made possible at the molecular level. Although the diagnosis of liver cancer is difficult to determine accurately by their pathological findings, it is possible to distinguish liver cancer from non-tumor liver tissue by differences in their molecular expression profiles. This is because the difference in molecular expression profiles makes it possible to select the exact set of genes involved in liver cancer development. It is also expected to facilitate setting appropriate targets for treatment and thus maximize the effectiveness of treating liver cancer.

In addition, in order to identify genes related to human cancers including liver cancers, studies to date have used the Unsupervised clustering algorithm, which is a very useful method to classify the total state by the whole pattern of gene expression. Developed. However, this unsupervised clustering analysis is not only difficult to provide statistical accuracy of the measurement results, but also has a problem that it is not easy to provide an accurate profile of specific forms of genes expressed from a lot of information of a given target. Even with the importance of the supervised learning algorithm, only a few recent reports have adopted learning algorithms for the overclassification of cancer and have found the clinical results of cancer patients.

Therefore, the present inventors have tried to develop a marker that can accurately confirm the level of hepatocellular carcinoma diagnosis and liver resection prognosis prediction at the molecular level. The present invention has been completed by identifying high liver cancer specific expression genes.

Accordingly, it is an object of the present invention to provide a composition for detecting markers for diagnosing liver cancer, comprising an agent for measuring the expression level of GGH (Gamma-glutamyl hydrolase).

Another object of the present invention is to provide a kit for detecting markers for diagnosing liver cancer, comprising the composition.

It is still another object of the present invention to provide a microarray for diagnosing liver cancer, including GGH (Gamma-glutamyl hydrolase).

Still another object of the present invention is to provide a method for detecting a liver cancer marker GGH (Gamma-glutamyl hydrolase).

As one aspect for achieving the above object, the present invention relates to a composition for detecting markers for diagnosing liver cancer, comprising an agent for measuring the expression level of GGH (Gamma-glutamyl hydrolase).

As used herein, the term "diagnostic" means identifying the presence or characteristic of a pathological condition. For the purposes of the present invention, the diagnosis is to determine whether the liver cancer.

As used herein, the term "diagnostic marker, diagnostic marker, or diagnostic marker" is a substance capable of diagnosing liver cancer cells from normal cells, and increases or decreases in cells with liver cancer compared to normal cells. Polypeptides or nucleic acids (such as mRNA), lipids, glycolipids, glycoproteins or organic biomolecules such as sugars (monosaccharides, disaccharides, oligosaccharides, etc.) and the like. For purposes of the present invention, the hepatic cancer diagnostic marker of the present invention is a GGH (Gamma-glutamyl hydrolase) gene and a protein encoded therein, which show a particularly high level of expression in liver cancer cells compared to cells of normal liver tissue (Fig. 5).

Gamma-glutamyl hydrolase (GGH) is also called folypolyglutamate hydrolase or conjugase, and its gene and protein information is registered in the National Center for Biotechnology Information (NCBI) (GeneID: 8836, NM_003878). GGH is known to hydrolyze gamma-polyglutamate, but the function of GGH and its association with liver cancer are not known at all.

The present inventors have found that GGH can be used as a marker for diagnosing liver cancer through the following verification.

Specifically, RNA was isolated and purified from 101 tumor tissues and 101 adjacent non-tumor tissues from liver cancer patients undergoing liver cancer surgery. On the other hand, RNA purified from placenta was used as a control RNA as a reference for use in DNA chip experiments. Thus, cDNA was prepared by reverse transcription of RNA and control RNA isolated from tissues, in which Cy5-dUTP and Cy3-dUTP were bound to cDNA, respectively (see FIG. 1). The reason why the control tissue was inserted in the middle was to increase the ease of analysis and to enable mutual comparison with other organizations of the analysis results. The hybridized DNA chip measured the intensity of Cy3 and Cy5 at each spot using a laser scanner. The relative ratios of these two types of fluorescence intensities were quantified using computer software (IMAGENE program) and the expression levels were measured.

Referring to the DNA microarray analysis method used in the present invention.

The quantified expression was normalized through the normalization process. Based on the normalized expression data, PAM (Prediction Analysis of Microarray) analysis and SAM (Significance Analysis of Microarray) were performed. PAM analysis and SAM analysis were used to predict the state of the sample using the microarray results. In the present invention, it was used to predict the difference between the surrounding non-tumor liver tissue and liver cancer tissue. In the case of PAM analysis, 40 liver cancer-specific genes were selected at threshold 5.5 as a result of comparing the error rate of cross-valuation according to each threshold value in order to select a group of liver cancer-specific genes predicting liver cancer tissue with the lowest error rate. As a result of performing cross-assessment of peripheral non-neoplastic liver tissue and liver cancer tissue using the selected gene, both of the peripheral non-neoplastic liver tissue and liver cancer tissue were predicted with an accuracy of 95% (see FIG. 2). In the case of SAM analysis, genes were selected through 1,000 virtual combinations (see FIG. 3), and 80 genes with increased expression in liver cancer tissues were selected. Genes with increased expression in liver cancer tissues were selected from both PAM and SAM assays. Genes with reduced expression in hepatic cancer tissues could be selected, but only those genes with increased expression in hepatic cancer tissues were analyzed.

Hierarchical clustering was performed to analyze the expression patterns of the selected genes. Hierarchical clustering (clustering) is a method to cluster the samples showing the most similar expression pattern according to the expression level of the measured gene. As a result of performing a clustering analysis to measure the expression of a liver cancer specific gene, it was confirmed that the surrounding non-tumoral liver tissues were clustered into one cluster similar in expression to each other (see FIG. 4). This shows that the prognosis of patients undergoing new liver cancer resection surgery can be predicted in advance by analyzing the expression patterns of the predictor genes, and based on this prediction, it can be used for the prognosis treatment of patients.

Meanwhile, liver cancer specific proteins discharged out of cells through the program were selected for a more effective diagnostic method such as easily measuring the presence or absence of liver cancer in the blood of the patient (see FIG. 5). The protein localization and estimation programs used were all used in Wolf PSORT (Nucleic Acid Research 2007) and SLPFA (BMC Bioinformatics 2007).

In the present invention, the term "agent for measuring the expression level of GGH (Gamma-glutamyl hydrolase)" refers to a molecule that can be used for detection of a marker by confirming the expression level of GGH, a marker for increasing expression in liver cancer cells as described above. Refers to antibodies, primers or probes that are specific for the marker.

The expression level of GGH can be known by identifying the expression level of the mRNA of the GGH gene or the protein encoded by the gene. In the present invention, "mRNA expression level measurement" is a process of confirming the presence and expression level of mRNA of liver cancer marker gene in a biological sample in order to diagnose liver cancer, and can be known by measuring the amount of mRNA. Analytical methods for this include RT-PCR, competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting (Northern) blotting), DNA chips and the like, but are not limited thereto. In the present invention, "protein expression level measurement" is a process of confirming the presence and expression level of the protein expressed in liver cancer marker gene in a biological sample for diagnosing liver cancer, the antibody specifically binding to the protein of the gene Check the amount of protein using. Analysis methods for this include Western blot, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, and rocket immunoelectrophoresis. , Tissue immunity staining, immunoprecipitation assay (Immunoprecipitation Assay), complement fixation assay (Complement Fixation Assay), FACS, protein chip (protein chip) and the like, but is not limited thereto.

Agents for measuring mRNA levels of genes are preferably primer pairs or probes, and since the nucleic acid sequence of the GGH gene is identified in NM_003878 (NCBI), those skilled in the art will recognize primers that specifically amplify specific regions of these genes based on the sequence. Alternatively, the probe can be designed.

As used herein, the term "primer" refers to a nucleic acid sequence having a short free 3 'hydroxyl group, which can form complementary templates and base pairs and is the starting point for template strand copying. It refers to a short nucleic acid sequence that functions as. Primers can initiate DNA synthesis in the presence of four different nucleoside triphosphates and reagents for polymerization (ie, DNA polymerase or reverse transcriptase) at appropriate buffers and temperatures. In the present invention, PCR can be performed using the sense and antisense primers of the GGH polynucleotide to diagnose liver cancer through the generation of a desired product. PCR conditions, sense and antisense primer lengths can be modified based on what is known in the art.

In the present invention, the term "probe" refers to nucleic acid fragments such as RNA or DNA, which are short to several bases to hundreds of bases, which are capable of specific binding with mRNA, and are labeled to identify the presence of a specific mRNA. Can be. The probe may be manufactured in the form of an oligonucleotide probe, a single stranded DNA probe, a double stranded DNA probe, an RNA probe, or the like. In the present invention, hybridization may be performed using a probe complementary to a GGH polynucleotide, and liver cancer may be diagnosed through hybridization. Selection of suitable probes and hybridization conditions can be modified based on what is known in the art.

Primers or probes of the invention can be synthesized chemically using phosphoramidite solid support methods, or other well known methods. Such nucleic acid sequences can also be modified using many means known in the art. Non-limiting examples of such modifications include methylation, “capsulation”, substitution with one or more homologs of natural nucleotides, and modifications between nucleotides, eg, uncharged linkages such as methyl phosphonates, phosphoesters , Phosphoramidates, carbamates, and the like) or charged linkages such as phosphorothioate, phosphorodithioate and the like.

Agents for measuring protein levels are preferably antibodies.

As used herein, the term "antibody" refers to a specific protein molecule directed to an antigenic site as it is known in the art. For the purposes of the present invention, an antibody means an antibody that specifically binds to GGH, a marker of the present invention, which is encoded by the marker gene by cloning each gene into an expression vector according to a conventional method. Proteins can be obtained and prepared from conventional proteins by conventional methods. Also included are partial peptides that may be made from such proteins, and the partial peptides of the present invention include at least seven amino acids, preferably nine amino acids, more preferably twelve or more amino acids. The form of the antibody of the present invention is not particularly limited and a part thereof is included in the antibody of the present invention and all immunoglobulin antibodies are included as long as they are polyclonal antibody, monoclonal antibody or antigen-binding. Furthermore, the antibody of this invention also contains special antibodies, such as a humanized antibody.

Antibodies used in the detection of liver cancer diagnostic markers of the invention include functional fragments of antibody molecules as well as complete forms having two full length light chains and two full length heavy chains. A functional fragment of an antibody molecule refers to a fragment having at least antigen binding function and includes Fab, F (ab '), F (ab') 2 and Fv.

As another aspect, the present invention relates to a marker detection kit for diagnosing liver cancer, comprising the composition for detecting liver cancer diagnostic marker.

The kit of the present invention can detect the marker by confirming the expression level of mRNA or protein for the expression level of liver cancer diagnostic marker GGH. The kit for detecting a marker of the present invention includes a primer, a probe, or an antibody that selectively recognizes a marker for measuring the expression level of a liver cancer diagnostic marker, as well as one or more other component compositions, solutions or devices suitable for analytical methods. Can be.

As a specific example, the kit for measuring the mRNA expression level of GGH in the present invention may be a kit containing the necessary elements necessary to perform RT-PCR. RT-PCR kits, in addition to each primer pair specific for the marker gene, RT-PCR kits can be used in test tubes or other appropriate containers, reaction buffers (variable pH and magnesium concentrations), deoxynucleotides (dNTPs), Taq-polymers. Enzymes such as lyase and reverse transcriptase, DNase, RNAse inhibitors, DEPC-water, sterile water, and the like. It may also include primer pairs specific for the gene used as the quantitative control. Also preferably, the kit of the present invention may be a kit for detecting a diagnostic marker including essential elements necessary for carrying out a DNA chip. The DNA chip kit may include a substrate to which a cDNA corresponding to a gene or a fragment thereof is attached with a probe, and the substrate may include a cDNA corresponding to a quantitative control gene or a fragment thereof.

As another specific example, the kit for measuring the protein expression level of GGH in the present invention includes a substrate, a suitable buffer, a secondary antibody labeled with a coloring enzyme or a fluorophore, a chromogenic substrate, and the like for immunological detection of the antibody. can do. The substrate may be a nitrocellulose membrane, a 96 well plate synthesized with a polyvinyl resin, a 96 well plate synthesized with a polystyrene resin, a slide glass made of glass, and the like. The chromophore may be a peroxidase or an alkaline force. Fatase (Alkaline Phosphatase) can be used, the fluorescent material can be used FITC, RITC, etc., the color substrate substrate is ABTS (2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) ) Or OPD (o-phenylenediamine), TMB (tetramethyl benzidine) can be used.

As another aspect, the invention relates to a microarray for diagnosing liver cancer, comprising a marker according to the invention. The microarray of the present invention can be easily prepared by a manufacturing method commonly used in the art using the marker of the present invention.

In another aspect, the present invention relates to a method for detecting liver cancer marker GGH by comparing the expression level of GGH from a sample of a patient to the expression level of normal cells in order to provide information necessary for diagnosing liver cancer.

More specifically, the expression level of the gene can be detected at the mRNA level or the protein level, and the separation of the mRNA or protein from the biological sample can be performed using a known process.

In the present invention, the term "patient sample" includes, but is not limited to, samples such as tissues, cells, whole blood, serum, plasma, saliva, sputum, cerebrospinal fluid or urine, which differ in the expression level of the liver cancer marker gene GGH. .

Through the detection methods, it is possible to diagnose whether the cancer patient is a real cancer patient by comparing the gene expression level in the normal control group with the gene expression level in the suspected liver cancer patient. That is, after measuring the expression level of the marker of the present invention from the cells suspected of liver cancer, and comparing the two by measuring the expression level of the marker of the present invention from normal cells, the expression level of the marker of the present invention is that of normal cells. If more expression is derived from a cell suspected of liver cancer, a cell suspected of liver cancer can be predicted as liver cancer.

In another aspect, the present invention compares the expression level of Gamma-glutamyl hydrolase (GGH) with a normal cell expression level from a sample of a patient undergoing liver cancer resection to provide the information necessary for predicting the prognosis of liver cancer resection. It relates to a method for detecting liver cancer marker GGH (Gamma-glutamyl hydrolase).

Through the detection method, it is possible to predict the prognosis of the patient who has undergone a new liver cancer resection surgery by analyzing the expression pattern of the predictive gene, and based on this prediction, it can be used for the prognosis treatment of the patient.

The present inventors identified genes showing different expression patterns between liver cancer and non-tumor liver tissues, and evaluated the genes of the proteins predicted to be released out of the cells among these genes and evaluated their utility as diagnostic markers. Therefore, the use of GGH, the liver cancer diagnostic marker according to the present invention, can accurately and easily measure the presence or absence of liver cancer, and furthermore, it can be utilized for the study of tumor formation of liver cancer, and can contribute to the early diagnosis of liver cancer. It seems to be.

Hereinafter, the present invention will be described in more detail with reference to Examples. Since these examples are only for illustrating the present invention, the scope of the present invention is not to be construed as being limited by these examples.

Example  One: Test group  And selection of test tissue samples

Test specimens were obtained from 101 patients who underwent surgery for liver cancer treatment at a nuclear hospital. Patients were informed to use surgical specimens and clinicopathological data for research purposes. All tissues were surgically extracted and immediately cooled in liquid nitrogen and stored at minus 80 ° C. RNA was isolated from cooled tissue by phenol / chloroform extraction according to the test manual (Trizol, Gibco / BRL). The quality of total RNA was determined from the band ratio between 28S and 18S RNA after agarose gel electrophoresis.

Example  2: cDNA Microarray

The microarrays used in the experiment consisted of a total of 14,080 human cDNA clones. Microarray experiments were performed using the 3DNA array detection kit (Genisphere Inc. PA, USA) according to the method suggested by the manufacturer. In brief, 10 μg of the experimental RNA and 10 μg of the normal control placental RNA, respectively, were reverse transcribed using reverse transcriptase and tagged with Cy5-dUTP and Cy3-dUTP, respectively, for 2 hours. Two types of cDNA were purified by passing through a microcon column (Millipore, Bedford, Mass.). Fluorescent cDNA purified from control and experimental samples was mixed with ExpressHyb ^™ hybrid solution (BD Biosciences Clontech, Palo Alto, Calif., USA) and reacted overnight at 65 ° C., followed by a scan array scanner (PerkinElmer, Boston, MA, USA). Scanned using.

Example  3: Data Analysis and Liver Cancer Specific Gene Screening

Digitized data was obtained from the scanned image file using IMAGENE 4.0 (Biodiscovery, Marina del Rey, Calif., USA), and normalized by considering the fluorescence intensity and the spot position (spatially dependent method). Genes not measured in at least 80% of the samples were excluded from subsequent analysis. Expression ratios were clustered hierarchically using the Cluster program and plotted using the treeview program. PAM (Prediction Analysis of Microarray) analysis and SAM (Significance Analysis of Microarray) analysis were used to select liver cancer specific genes. In the case of PAM analysis, the predictive evaluation was performed after selecting the group of genes showing the least error in the cross-valuation. The error rate here is the overall error rate as to whether the liver cancer tissue is correctly predicted as liver cancer tissue or the surrounding non-tumor liver tissue as the surrounding non-tumor liver tissue. According to the threshold value, the lowest error rate exists, and a gene list corresponding to the value can be obtained, and this gene list can predict liver cancer with the lowest error rate. As a result of predicting the state of individual samples using the gene list corresponding to the threshold value determined above, the sample was accurately predicted with a 95% probability. In the SAM analysis, genes with the highest scores (genes measured higher than random probability of virtual combinations) were sequentially selected after 1,000 hypothetical combinations.

The selected genes were selected in the following manner for more effective diagnosis. First, the distribution map was prepared for each protein through the program, and only the proteins secreted out of the cell were selected as diagnostic genes (FIG. 5). The protein localization and estimation programs used were all used in Wolf PSORT (Nucleic Acid Research 2007) and SLPFA (BMC Bioinformatics 2007).

Example  4: Predicting Liver Cancer by Predictive Gene

As a result of performing cross-assessment of PAM analysis, the prediction accuracy was 95%, which shows that the gene discrimination of liver cancer is very accurate (see FIG. 2). Liver cancer specific genes obtained by PAM analysis were clustered by the Unsupervised clustering algorithm method. As a result of the clustering, it was confirmed that the surrounding non-tumorigenic liver tissues were clustered into one group by the expression pattern of the predictor genes (see FIG. 4). This shows that the prognosis of patients undergoing new liver cancer resection surgery can be predicted in advance by analyzing the expression patterns of the predictor genes, and based on this prediction, it can be used for the prognosis treatment of patients.

Figure 1 is a schematic diagram showing the hybridization and analysis of the reverse transcription of mRNA and DNA chip for measuring the expression profile of liver cancer diagnostic gene.

FIG. 2 is a diagram illustrating the accuracy of predictions measured by performing cross-valuation using the selection criteria for the genes obtained from FIG. 1.

3 is a diagram illustrating the distribution of genes by combinatorial analysis using selection criteria for the genes obtained from FIG. 1.

4 is a schematic of expression profiling of selected liver cancer specific genes.

Figure 5 shows the gene sequence and protein sequence of the selected liver cancer diagnostic gene GGH.

Claims (8)

A composition for detecting markers for diagnosing liver cancer, comprising an agent for measuring the expression level of GGH (Gamma-glutamyl hydrolase). The composition of claim 1, wherein said agent is an antibody. The composition of claim 1 wherein said agent is a primer. The composition of claim 1, wherein said agent is a probe. A kit for detecting a marker for diagnosing liver cancer, comprising the composition of any one of claims 1 to 4. Microarray for diagnosing liver cancer, including GGH (Gamma-glutamyl hydrolase). A method of detecting liver cancer marker GGH (Gamma-glutamyl hydrolase) by comparing the expression level of GGH (Gamma-glutamyl hydrolase) from a patient's sample to provide information necessary for diagnosing liver cancer. To provide the information necessary for predicting the prognosis of liver cancer resection, the expression level of GGH (Gamma-glutamyl hydrolase) was compared with that of normal cells in the sample of patients undergoing liver cancer resection, and the liver cancer marker GGH (Gamma-glutamyl hydrolase) How to detect it.

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