KR20070083255A - Sybl1 and tm9sf1, the markers for diagnosing cervix cancer, a kit comprising the same and method for predicting cervix cancer using the markers - Google Patents

Abstract

Provided is a marker for diagnosing cervix cancer containing SYBL1 and TM9SF1 which shows different expression pattern between cervix cancer and non-tumor uterine tissue, thereby being utilized for studying the tumor formation of the cervix cancer and significantly contributing to an early diagnosis of the cervix cancer. The marker for diagnosing cervix cancer comprises a polynucleotide of NM_005638(SYBL1; synaptobrevin-like-1) and NM_006405(TM9SF1; transmembrane 9 superfamily member 1). The method for predicting cervix cancer comprises the steps of: (a) measuring the expression level of at least one of the polynucleotide selected from SYBL1 and TM9SF1 from uterine sarcoma cells; and (b) comparing the measured expression level with the expression level of normal cells.

Description

SYBL1 and TM9SF1, the markers for diagnosing cervix cancer, a kit comprising the same and method for predicting cervix cancer using the markers}

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 the uterine cancer diagnostic gene.

2 is a schematic showing the error of cross-assessment for selecting uterine cancer specific genes.

3 is a diagram illustrating the accuracy of predictions measured by performing cross-evaluation using the gene selection criteria obtained from FIG. 2.

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

5 lists selected uterine cancer diagnostic genes.

The present invention relates to a marker for diagnosing uterine cancer, a kit and microarray comprising the same, and a method for predicting uterine cancer using the marker.

Cervical cancer, which accounts for more than 90% of uterine cancers, is the most common malignant tumor among Korean women. In particular, infections of human papillomavirus (HPV) types 16 and 18 have been found to be the most important factors in the pathogenesis of carcinogenesis, and among patients with uterine cancer, the infection rate of HPV 16 is about 50-70%. It is reported that the infection rate of type 18 reaches 15-25%, but compared to the case of metastatic cancer, about 25% of type 16 infected patients and more than 50% of type 18 infected patients have metastatic cancer. According to data released in July 1993 by the US National Cancer Institute, uterine cancer is now the number one in female cancer in developing countries, with an estimated 500,000 new cases each year. It is. In Seoul, Korea, 123 people per 100,000 women are diagnosed with cancer each year. Among them, uterine cancer is 22%, gastric cancer 18%, and breast cancer 12.9%. have. In other words, the incidence of uterine cancer in Korea is 27 out of every 100,000 women, about three times higher than in the United States, especially in advanced countries. Mortality rates are also expected to be higher. The incidence rate of uterine cancer can be reduced by more than two-thirds even if it can increase the regular screening rate by early diagnosis. Therefore, it is urgent to develop a diagnostic agent along with the development of preventive and therapeutic vaccines to improve public health.

The application of cDNA microarray technology has made molecular diagnostics possible at the molecular level by measuring genes actively involved in the development of human cancers, including uterine cancer, and measuring proteins or enzymes expressed in those genes. Although the diagnosis of uterine cancer is difficult to determine accurately by their pathological findings, it is possible to distinguish uterine cancer from non-tumor uterine tissues by differences in their molecular expression profiles. This is because differences in molecular expression profiles make it possible to select the correct set of genes involved in uterine cancer development. This may also facilitate setting appropriate goals for treatment and thus maximize the effectiveness of treatment for uterine cancer.

In order to identify genes related to human cancers, including uterine cancer, studies to date have developed an unsupervised clustering algorithm, a very useful method for classifying the total state by the whole pattern of gene expression. However, this unsupervised clustering analysis was not only difficult to provide statistical accuracy of the measurement, but it was also difficult 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 a learning algorithm for the overclassification of cancer and have found clinical outcomes for cancer patients.

Accordingly, the present inventors have completed the present invention by identifying genes specifically expressed in uterine cancer through cDNA microarray analysis.

The present invention is intended to obtain robust information on the development of uterine cancer, and to apply the uncontrolled or controlled running approach, using cDNA microarray to determine the gene expression pattern of uterine cancer and the gene expression pattern of non-tumor uterine tissues associated with them It was investigated. In particular, the accuracy of discriminating the predictor gene and uterine cancer selected by the pass of the training observation can be established by cross-validation and can also be evaluated by applying an independent test to a set of uterine cancer samples. . In addition, the results of the expression profile may provide detailed informed guesses of tumor suppression, detoxification and lipid metabolism during the development of uterine cancer, which provides markers and molecular targets suitable for the diagnosis and treatment of uterine cancer. It is.

Accordingly, it is an object of the present invention to provide a marker for diagnosing uterine cancer comprising at least one polynucleotide selected from the group consisting of two polynucleotides described in FIG. 5.

Another object of the present invention is to provide a kit and a microarray comprising the marker of the present invention.

Another object of the present invention is to provide a method for predicting uterine cancer using the marker of the present invention.

The present invention provides a marker for diagnosing uterine cancer comprising 1) NM_005638 (SYBL1; synaptobrevin-like 1) or 2) NM_006405 (TM9SF1; transmembrane 9 superfamily member 1).

In order to diagnose uterine cancer, the present invention separates the uterine tissue of a uterine cancer patient and measures the degree of protein expression on the surface using immunohistochemistry to determine whether the uterine cancer is based on this.

Specifically, 52 tumor tissues were collected from uterine cancer patients undergoing uterine cancer surgery, and RNA was isolated and purified. 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. PAM (Prediction Analysis of Microarray) analysis was performed based on the normalized expression data. PAM analysis is a program that can predict the state of the sample using the microarray results, in the case of the present invention was used to predict the difference between myoma tissue and uterine cancer tissue. In order to select a group of uterine cancer specific genes predicting uterine cancer tissue with the lowest error rate, as compared with the error rate of cross evaluation according to each threshold value (see FIG. 2), 140 uterine cancer specific genes were selected at threshold 2.7. Cross-assessment of the myoma tissue and the uterine cancer tissue using the selected gene was predicted with 100% accuracy of both the myoma and the uterine cancer tissue (see Fig. 3). Hierarchical clustering was performed to analyze the expression patterns of the selected genes (see FIG. 4). 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 the uterine cancer specific genes, it was confirmed that the myoma tissues are clustered into one cluster similar in expression to each other (see FIG. 4).

This shows that the prognosis of the patient who has undergone new hysterectomy can be predicted in advance by analyzing the expression pattern of the predictor gene, and based on this prediction, it can be used for the prognosis of the patient.

For more effective diagnostic methods, such as easily measuring the presence or absence of uterine cancer in the patient's blood or using it as an imaging marker, it is present on the cell membrane through the http://psort.ims.u-tokyo.ac.jp/form2.html program. Two types of uterine cancer specific proteins were selected.

The marker of the present invention is a polynucleotide capable of diagnosing uterine cancer, and refers to one or more polynucleotides selected from the group consisting of two polynucleotides described in FIG. 5, and preferably includes two sets of polynucleotides described in FIG. 5. . In other words, by using both sets of two polynucleotides, uterine cancer can be diagnosed more efficiently.

The marker of the present invention can be used for imaging diagnosis of uterine cancer. That is, a marker expressed in uterine cancer using the imaging marker of the present invention can be directly detected by an imager using a labeled antibody. Therefore, when the labeled antibody is injected into the patient and photographed directly with the imager, the patient can directly diagnose the uterine cancer.

Markers of the invention may be therapeutic targets for uterine cancer. Since many markers of the present invention are expressed in uterine cancer patients, uterine cancer may be treated by controlling the expression of the markers. Thus, the marker may be a therapeutic target of uterine cancer.

The invention also provides a kit for diagnosing uterine cancer, comprising the marker according to the invention.

Kits of the invention may also comprise sense and antisense primers of one or more polynucleotides selected from the group consisting of the polynucleotides described in FIG. By performing PCR amplification using the sense and antisense primers of the polynucleotide, uterine cancer can be diagnosed 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.

Kits of the invention may also comprise probes complementary to one or more polynucleotides selected from the group consisting of the polynucleotides described in FIG. Hybridization may be performed using a probe complementary to the polynucleotide, and uterine cancer may be diagnosed through hybridization. Selection of suitable probes and hybridization conditions can be modified based on what is known in the art.

Kits of the present invention may also comprise antibodies that recognize a protein encoded by one or more polynucleotides selected from the group consisting of the polynucleotides described in FIG. When the antibody labeled with fluorescence (for example, green fluorescence protein (GFP)) or radiation of the present invention is injected into uterine cancer tissue and combined with the protein, the signal is detected by a detector capable of detecting fluorescence or radiation. Can diagnose uterine cancer directly. That is, a marker expressed in uterine cancer using the imaging marker of the present invention can be directly detected by an imager using a labeled antibody. Therefore, when the labeled antibody is injected into the patient and photographed directly with the imager, the patient can directly diagnose the uterine cancer. Green fluorescent protein gene is a gene isolated from marine luminescent jellyfish (fluorescent jellifish), which is already commercialized and used in various medical research as well as biology. In addition, it is also possible to diagnose uterine cancer by separating the uterine tissue of the uterine cancer patient.

The kit may comprise the antibody, substrate, a suitable buffer, a secondary antibody labeled with a chromophore or a fluorescent substance, a chromogenic substrate, and the like. 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.

Antigen-antibody binding reactions can be measured by enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), sandwich assay, western blot or immunoblot assay on polyacrylamide gels. Can be.

The antibody to the polynucleotide for diagnosing uterine cancer of the present invention can be produced by a conventional method from the protein obtained by cloning each gene into an expression vector according to a conventional method, to obtain a protein encoded by the marker gene. This includes partial peptides that can be made from the protein, and the partial peptide of the present invention includes at least 7 amino acids, preferably 9 amino acids, more preferably 12 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.

The invention also provides a microarray for diagnosing uterine cancer, comprising the 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.

The invention also includes measuring the expression level of one or more polynucleotides selected from the group consisting of the polynucleotides described in FIG. 5 from uterine cancer cells and comparing the measured expression levels with expression levels of normal cells. Provides a method of predicting uterine cancer. That is, after measuring the expression level of the marker of the present invention from cells suspected of uterine cancer, 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 uterine cancer, a cell suspected of uterine cancer can be predicted as uterine cancer.

The present invention also comprises the steps of measuring the expression level of at least one polynucleotide selected from the group consisting of polynucleotides of 1) and 2) from uterine cancer cells; And comparing the measured expression level with the expression level of normal cells. This shows that by predicting the expression patterns of predictive genes, we can predict the prognosis of patients who have undergone new hysterectomy and can be used for the prognosis of patients based on these predictions.

In this method, the polynucleotide comprises the two polynucleotide sets described in FIG. In other words, by using both sets of two polynucleotides, uterine cancer can be predicted more efficiently.

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 73 patients who underwent surgery for treatment of uterine cancer 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 the 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 experimental RNA and 10 μg of normal control placental RNA, respectively, were reverse transcribed using reverse transcriptase and tagged with Cy5-dUTP and Cy3-dUTP for 2 hours, respectively. 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 Uterine 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 was used to select uterine cancer specific genes. Predictive evaluation was performed after selecting the group of genes that showed the least error in cross evaluation. Figure 2 shows the error rate of the overall cross validation (cross validation). The threshold on the x-axis is a statistical value that determines how many genes are selected. The lower the threshold, the more genes, and the higher the threshold, the fewer genes. 2 shows the error rate for this threshold value. The error rate here is the overall error rate as to whether the uterine cancer tissue is accurately predicted as uterine cancer tissue or myoma tissue as myoma tissue. As shown in FIG. 2, the lowest error rate exists according to the threshold value. Therefore, a list of genes corresponding to the value can be obtained, and this list of genes can predict uterine cancer with the lowest error rate. 3 shows a breakdown of the error rates of individual tissues. The results of predicting the status of individual myoma tissue and uterine cancer tissue using the gene list corresponding to the threshold value determined in FIG. 2 are shown in FIG. 3. The probability of accuracy is the cross-evaluation probability that the y axis is, and the x axis is the individual sample. That is, as a result of predicting the state of the individual samples based on the gene list selected in FIG. 2, the probability of predicting the myoma tissue as the myoma tissue and the probability of predicting the uterine cancer tissue as the uterine cancer tissue are measured. As shown in FIG. 3, most samples were accurately predicted with a 100% probability. Only about 1 percent of uterine cancer tissues have been predicted.

The selected genes were selected in the following manner for more effective diagnosis. First, the intracellular distribution of each protein was prepared through the http://psort.ims.u-tokyo.ac.jp/form2.html program. Of these, only proteins present in the plasma membrane were selected as diagnostic genes (FIG. 5).

Example  4: Predicting uterine cancer by predictive gene

Uterine cancer specific genes obtained by PAM analysis were clustered by the Unsupervised clustering algorithm method (FIG. 3). As a result of the clustering, it was confirmed that the myoma tissues were clustered into one group by the expression pattern of the predictor genes (FIG. 4). As a result of the cross-evaluation, the prediction accuracy was 100%, which shows that the genetic discrimination of uterine cancer is very accurate.

In accordance with the present invention, it is demonstrated that a set of predictive genes for discriminating uterine cancer from non-tumor uterine tissue can be established by applying a supervisored learning algorithm. In addition, it was noteworthy that the decrease-regulatory genes were more in uterine cancer than the increase-regulatory gene, which resulted in an asymmetric distribution pattern in their expression profile between uterine cancer and non-tumor uterine tissue. Although unsupervised clustering is very useful for classifying human cancers through the entire pattern of gene expression, there are many difficulties in selecting the optimal predictive gene family. Therefore, in the current study, we used the PAM assay, a supervised learning method, to select more important genes specific for uterine cancer. Cross-assessment of uterine cancer and myoma sample sets identified 140 specific types of expressed genes. In order to more efficiently perform the purpose of diagnosing uterine cancer by the gene expression pattern, a gene group predicted to be present in the cell membrane of the uterine cancer specific gene group was selected. If the gene group of the selected protein expected to be released is used as an image diagnosis marker, the accuracy of the existing image diagnosis method can be further enhanced.

In conclusion, we identified genes representing different expression patterns between uterine cancer and non-tumor uterine tissue. This confirmed gene can be used for the study of tumor formation of uterine cancer, and it is considered to be a great contributor to the early diagnosis of uterine cancer.

Claims (12)

1) NM_005638 (SYBL1; synaptobrevin-like 1) or 2) NM_006405 (TM9SF1; transmembrane 9 superfamily member 1) A marker for diagnosing uterine cancer, comprising the polynucleotide of. The method of claim 1, Marker comprising a set of polynucleotides of 1) and 2). The method according to claim 1 or 2, The marker is characterized in that it is used for imaging of uterine cancer. The method according to claim 1 or 2, The marker is characterized in that the therapeutic target of uterine cancer. A kit for diagnosing uterine cancer, comprising the marker according to claim 1. The method of claim 5, And a sense and antisense primer of at least one polynucleotide selected from the group consisting of polynucleotides of 1) and 2). The method of claim 5, And a probe complementary to at least one polynucleotide selected from the group consisting of polynucleotides 1) and 2). The method of claim 5, Kit comprising a antibody that recognizes a protein encoded by at least one polynucleotide selected from the group consisting of polynucleotides of 1) and 2). A microarray for diagnosing uterine cancer, comprising the marker according to claim 1. Measuring the expression level of at least one polynucleotide selected from the group consisting of polynucleotides 1) and 2) from uterine cancer cells; And Comparing the measured expression level with the expression level of normal cells. Measuring the expression level of at least one polynucleotide selected from the group consisting of polynucleotides 1) and 2) from uterine cancer cells; And Comparing the measured expression level with the expression level of normal cells. The method of claim 10 or 11, The polynucleotide is a set of polynucleotides 1) and 2).

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