KR20070097934A - A marker for predicting survival period of a hepatocellular carcinoma patient, a kit and microarray comprising the same and method for predicting survival period of a hepatocellular carcinoma patient using the marker - Google Patents

A marker for predicting survival period of a hepatocellular carcinoma patient, a kit and microarray comprising the same and method for predicting survival period of a hepatocellular carcinoma patient using the marker Download PDF

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KR20070097934A
KR20070097934A KR1020060028890A KR20060028890A KR20070097934A KR 20070097934 A KR20070097934 A KR 20070097934A KR 1020060028890 A KR1020060028890 A KR 1020060028890A KR 20060028890 A KR20060028890 A KR 20060028890A KR 20070097934 A KR20070097934 A KR 20070097934A
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이기호
김부여
권희충
박명진
최수용
함용호
이은주
한철주
김상범
박선후
최동욱
김창민
정숙향
정하현
서경석
장자준
김광중
이제근
염영일
양석진
유향숙
김남순
김용성
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한국생명공학연구원
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Abstract

Markers for predicting survival period of a hepatocellular carcinoma patient is provided to identify genes capable of predicting survival period of a hepatocellular carcinoma patient after hepatectomy through analysis of cDNA(complementary DNA) microarray. A marker for predicting survival period of a hepatocellular carcinoma patient after hepatectomy is identified by binding Cy5-dUTP to cDNA prepared by performing reverse transcription of mRNA(messenger RNA) extracted from a hepatocellular carcinoma cell, binding Cy3-dUTP to cDNA prepared by performing reverse transcription of mRNA extracted from the placenta as a control, mixing cDNA of hepatocellular carcinoma cell with cDNA of placenta, hybridizing the mixture with a DNA microarray, and analyzing the hybridization results with a computer.

Description

간암 환자 생존기간 예측용 마커, 그를 포함하는 키트 및 마이크로어레이, 및 상기 마커를 이용한 간암 환자 생존기간 예측 방법{A marker for predicting survival period of a hepatocellular carcinoma patient, a kit and microarray comprising the same and method for predicting survival period of a hepatocellular carcinoma patient using the marker}A marker for predicting survival period of a hepatocellular carcinoma patient, a kit and microarray comprising the same and method for predicting survival period of a hepatocellular carcinoma patient using the marker}

도 1은 간암 수술 후 생존 예측유전자의 발현 프로파일을 측정하기 위한 mRNA의 역전사와 DNA 칩에서 혼성화 및 분석 과정을 나타낸 모식도이다.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 survival predictor gene after liver cancer surgery.

도 2는 선별된 생존관련 예측유전자를 열거한 것이다.2 lists selected survival-related predictors.

도 3은 선별된 생존관련 예측유전자의 발현 프로파일링의 도식화이다.3 is a schematic of expression profiling of selected survival-related predictors.

도 4는 프로파일링에 의해 클러스터링된 환자군의 생존율을 비교 분석한 것이다.4 is a comparative analysis of the survival rate of the clustered patient group by profiling.

도 5는 신규 환자군에 대한 생존관련 예측유전자의 발현 프로파일링의 도식화이다.5 is a schematic of expression profiling of survival-related predictors for new patient groups.

도 6은 예측유전자의 프로파일링에 의한 신규 환자군의 생존율을 예측한 것이다.Figure 6 predicts the survival rate of a new patient group by profiling the predictor gene.

본 발명은 간암 환자 생존기간 예측용 마커, 그를 포함하는 키트 및 마이크로어레이, 및 상기 마커를 이용한 간암 환자 생존기간 예측 방법에 관한 것이다.The present invention relates to a marker for predicting liver cancer patient survival time, a kit and microarray including the same, and a method for predicting liver cancer patient survival time using the marker.

간암 (hepatocellular carcinoma)은 세계적으로 가장 치명적인 암의 하나로, 특히 아시아와 사하라 이남 아프리카에서, 해마다 약 오십만 명 이상이 간암으로 사망하고 있다. 비록 간암의 위험 요인이 B형 간염 또는 C형 간염 바이러스에 의한 고질적인 감염이라고 할지라도 간암 세포 내의 분자 메커니즘은 아직도 명확히 규명되지 않은 상태이다. 최근의 연구에 의하면 간암 발생에 있어서 변형된 p53, 베타-카테닌, AXINI, p21(WAF1/CIP1) 및 p27 Kip 등의 유전자가 관련된다는 것이 밝혀졌다. 그러나 이러한 개개의 유전자의 변화들은 간암 환자들의 외적 특성과 임상 특성을 정확하게 반영하지 못하고 있다. 개체의 간암 내의 세포와 분자의 다양성은 기존의 유전자 연구 외에 새로운 접근 방식을 요구하고 있다. 이와 관련하여 마이크로어레이 테크놀로지는 개개의 유전자의 측정범위를 넘어서 한번의 실험으로 수만여 개의 유전자 발현을 동시에 측정할 수 있는 새로운 기술로서 간암을 포함한 거의 대부분의 암 연구에 적용되어 왔으며, 암의 발생 및 진행과정에 활발하게 참여하는 유전자를 추출함으로써 암 진단 및 예후예측의 수준을 분자 수준에서 가능하도록 하였다.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 chronic infection by hepatitis B or hepatitis C virus, the molecular mechanisms within the liver cancer cells are still unclear. Recent studies have shown that genes such as p53, beta-catenin, AXINI, p21 (WAF1 / CIP1) and p27 Kip are involved in the development of liver cancer. However, these individual gene changes do not accurately reflect the external and clinical characteristics of liver cancer patients. The diversity of cells and molecules within individual liver cancers requires new approaches in addition to conventional genetic studies. In this regard, microarray technology is a new technology that can simultaneously measure the expression of tens of thousands of genes in a single experiment beyond the range of individual genes, and has been applied to almost all cancer research including liver cancer. By extracting genes that actively participate in the process, the level of cancer diagnosis and prognosis is made possible at the molecular level.

현재 간 절제술에 의한 치료를 받고 있는 간암 환자는 전체 간암 환자의 약 20% 내외이다. 간 절제술은 간암 환자 중에서 전이가 발생하지 않은 비교적 초기 환자의 치료법으로 사용되고 있다. 하지만 간 절제술을 받은 간암 환자의 경우에 도 장기 생존율은 높지 않은 편이며, 특히 수술 후 1년 안에 사망하는 환자가 많다. 임상 병리분석에 의한 생존율 예측이 예전부터 사용되고 있으나, 보편성과 타당성의 문제로 인해 모든 간암 환자에게 적용되지 못하고 있다.Currently, about 20% of all liver cancer patients are treated with liver resection. Liver resection is used as a treatment for relatively early patients without metastases among liver cancer patients. However, long-term survival rate is low even in liver cancer patients who have undergone liver resection, and many patients die within one year after surgery. Survival prediction based on clinical pathology has been used for a long time, but it is not applicable to all liver cancer patients due to problems of universality and validity.

간암을 포함한 인간 암에 관련된 유전자를 마이크로어레이 방법으로 규명하기 위하여 현재까지의 연구들은 유전자 발현의 패턴을 분석하는데 매우 유용한 방법인 언수퍼바이즈드 클러스터링 알고리즘(unsupervised clustering algorithm)및 수퍼바이즈드 알고리즘을 개발하였다. 언수퍼바이즈드 클러스터링 분석은 샘플 내에 존재하는 내재적인 생물학적 의미를 추출하는데 매우 유용하나, 그 측정결과의 통계적인 정확성을 제공하기 어려울 뿐 아니라, 측정되는 유전자의 수를 적절하게 조절하기 힘든 단점이 있다. 수퍼바이즈드 러닝 알고리즘(supervised learning algorithm)의 경우 샘플을, 차이를 보고자 하는 군으로 분류하여 분석할 수 있다는 매우 강력한 장점 외에도 분석 결과를 이용하여 신규 샘플을 검증할 수 있다는 특징을 가지고 있다. 최근에는 간암을 포함한 다양한 암의 마이크로어레이 프로파일링 분석에 이 두 가지 방법이 모두 적용되고 있으며, 신규 샘플의 진단 및 예측에 모두 성공적으로 사용되고 있다.In order to identify genes related to human cancers including liver cancer by the microarray method, studies to date have been very useful for analyzing patterns of gene expression, unsupervised clustering algorithm and supervised algorithm. Developed. Unsupervised clustering analysis is very useful for extracting intrinsic biological meanings present in a sample, but it is difficult to provide statistical accuracy of the measurement results, and it is difficult to properly control the number of genes measured. have. The supervised learning algorithm has the advantage of being able to classify and analyze the samples into groups that want to see the differences, as well as the ability to verify new samples using the analysis results. Recently, both methods have been applied to the analysis of microarray profiling of various cancers, including liver cancer, and have been successfully used for diagnosis and prediction of new samples.

간암에서 유전자 발현 프로파일의 분석은 마이크로어레이를 이용한 몇몇의 연구에서 이미 보고되었다. 하지만 유전자 프로파일을 이용한 간암 수술 후 생존예측과 같은 연구는 거의 시행되고 있지 않다. 마이크로어레이를 이용한 간암환자의 생존 예측연구가 거의 전무한 반면, 기존의 임상병리학적 기준에 따른 간암환자의 위험도 분석은 이미 오래전부터 연구되어 오고 있으며 그 효용성에 대해 전세계 의 평가가 현재 이루어지고 있는 중이다. 임상항목에 따른 간암환자의 위험도 예측은 3-4 개의 임상항목의 수치적 평가를 합산해서 평가하는 방식을 따르고 있다. 이 방식은 그 간결함 때문에 많이 이용되고 있으나, 그 정확도와 보편성에서 많은 문제점을 가지고 있다. 임상항목 수치의 주관적인 판단, 간암의 다양성을 평가하지 못하는 문제 등이 그 주원인이다. 본 연구는 이런 단점을 극복하기 위해, 간암 조직의 유전자 발현 패턴이라는 객관적인 요소를 바탕으로 간암환자를 분류함으로써 주관적 요소를 배제하였으며, 1-2 개 유전자의 발현도를 평가하는 것이 아니라 수십 개의 유전자의 발현 패턴을 동시에 측정하는 프로파일방식을 사용하였다.Analysis of gene expression profiles in liver cancer has already been reported in several studies using microarrays. However, few studies have been conducted to predict survival after liver cancer surgery using gene profiles. While there have been few studies on predicting survival of liver cancer patients using microarrays, the risk analysis of liver cancer patients according to the existing clinicopathological criteria has been studied for a long time, and the evaluation of the efficacy of the liver cancer is being conducted worldwide. The risk prediction of liver cancer patients according to the clinical items follows the method of summating the numerical evaluations of 3-4 clinical items. This method is widely used because of its simplicity, but has many problems in its accuracy and universality. The main causes are the subjective judgment of the clinical item value and the inability to evaluate the diversity of liver cancer. To overcome these shortcomings, we excluded subjective factors by classifying liver cancer patients based on objective factors called gene expression patterns of liver cancer tissues, and dozens of gene expressions rather than evaluating the expression level of 1-2 genes. The profile method of measuring a pattern simultaneously was used.

따라서, 본 발명자들은 cDNA 마이크로어레이 분석을 통하여 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 유전자를 규명함으로써, 본 발명을 완성하게 되었다.Therefore, the present inventors have completed the present invention by identifying genes capable of predicting the survival after surgery of a person who has undergone liver resection for liver cancer through cDNA microarray analysis.

본 발명은 간암 (hepatocellular carcinoma)으로 판명된 환자들 중 간 절제술의 치료를 받은 사람의 생존기간을 마이크로어레이를 통한 유전자 발현의 프로파일링을 통해 예측하고자 하는 것이다. 마이크로어레이를 이용해 선별한 생존관련 예측유전자는 교차평가(cross validation) 및 예측분석 (predicting)을 통해 그 효용성이 유의적임을 보여야 하며, 실제 임상에 적용할 수 있을 정도의 수월성, 보편성 및 타당성을 확보해야 한다.The present invention aims to predict the survival of patients who have been treated for hepatocellular carcinoma through profiling of gene expression through microarray. Survival-related predictors selected using microarrays should be shown to have significant utility through cross-validation and predictive analysis, and ensure their excellence, universality and validity to be applicable to actual clinical trials. Should be.

따라서, 본 발명의 목적은 도 2a에 기재된 50개의 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 마커를 제공하는 것이다.Accordingly, it is an object of the present invention to provide a marker that can predict postoperative survival of a person who has undergone liver resection for liver cancer, comprising one or more polynucleotides selected from the group consisting of the 50 polynucleotides described in FIG. 2A. .

본 발명의 다른 목적은 본 발명의 마커를 포함하는 키트 및 마이크로어레이를 제공하는 것이다.Another object of the present invention is to provide a kit and a microarray comprising the marker of the present invention.

본 발명의 또 다른 목적은 본 발명의 마커를 이용하여 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측하는 방법을 제공하는 것이다.Still another object of the present invention is to provide a method for predicting survival after surgery of a person who has undergone liver resection for liver cancer using the marker of the present invention.

본 발명은 도 2a에 기재된 50개의 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 마커를 제공하는 것이다.The present invention provides a marker capable of predicting the postoperative survival of a person who has undergone liver resection for liver cancer, comprising one or more polynucleotides selected from the group consisting of the 50 polynucleotides described in FIG. 2A.

본 발명은 간 절제술을 받은 간암환자의 생존기간의 예측을 위한 것으로, 간암세포에서 분리 추출한 mRNA를 역전사시켜 수득한 cDNA에 Cy5-dUTP를 결합시키고, 대조군으로 태반에서 분리 정제한 mRNA를 역전사시켜 수득한 cDNA에 Cy3-dUTP를 결합시켜 혼합한 후, DNA 마이크로어레이와 혼성화시켜 그 결과를 컴퓨터 소프트웨어를 이용하여 처리하여 간암 환자의 생존기간을 예측할 수 있는 유전자를 탐색한다.The present invention is for predicting the survival time of liver cancer patients undergoing liver resection, the CyDa-DUTP is coupled to cDNA obtained by reverse transcription of mRNA isolated from liver cancer cells, and obtained by reverse transcription of purified purified mRNA from placenta as a control. Cy3-dUTP is bound to and mixed with a cDNA, hybridized with DNA microarrays, and the results are processed using computer software to find genes that can predict the survival of patients with liver cancer.

구체적으로, 간암 환자 중 B형 간염 보균자이며 간암수술을 받은 환자로부터 종양조직 73개를 채취하여 RNA를 분리 정제하였다. 한편, DNA 칩 실험에 사용할 기준으로 태반으로부터 정제한 RNA를 대조 RNA로 사용하였다. 이렇게 조직으로부터 분리한 RNA와 대조 RNA를 역전사(reverse transcription)시켜 cDNA를 제조하였는데, 이 과정에서 Cy5-dUTP, Cy3-dUTP를 cDNA에 각각 결합하도록 하였다 (도 1 참 고). 이렇게 대조 조직을 중간에 삽입시킨 이유는 분석의 용이성을 증대시키고, 분석 결과의 타 기관과의 상호 비교를 가능하게 하기 위해서였다. 이렇게 혼성화시킨 DNA 칩은 레이저 스캐너를 이용하여 각 스폿에서의 Cy3 와 Cy5의 강도 (intensity)를 측정하였다. 이 두 가지 종류의 형광강도의 상대적인 비율을 컴퓨터 소프트웨어(IMAGENE program)를 이용하여 수치화하여 발현도를 측정하였다.Specifically, RNA was isolated and purified from 73 tumor tissues from hepatitis B carriers and liver cancer patients. 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 the process, 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 was measured by using a laser scanner to measure the intensity of Cy3 and Cy5 at each spot. The relative ratios of these two types of fluorescence intensities were quantified using computer software (IMAGENE program) and the expression levels were measured.

본 발명에 사용된 DNA 마이크로어레이 분석방법을 설명하면 다음과 같다.Referring to the DNA microarray analysis method used in the present invention.

간암 절제술 이후 추적한 환자의 생존기간을 측정한 뒤 종속변수로 활용하여, 콕스회귀분석 (Cox regression analysis)을 수행하였다. 한편, 상기한 마이크로어레이에 의해 측정된 유전자 발현도를 독립변수로 활용하여 생존기간과 관련된 유전자를 통계적으로 분류 정렬하였다. 이렇게 통계적으로 정렬된 유전자 중 상위 156개의 유전자를 선별하였다 (도 2). 유전자들은 모두 서열이 공지되어 있는 것으로, 미국 NIH에서 주관하는 GenBank에 등록되어 있다. 따라서, 상기 유전자들은 등록번호(accession number)를 입력하여 GenBank로부터 그 유전자 서열을 입수할 수 있으며, 등록번호는 도 2에 기재되어 있다. 이들 유전자들을 예측유전자군으로 명명한 후, 계층적 클러스터링을 수행하였다 (도 3 참고). 계층적 클러스터링 (clustering) 방법은 측정한 유전자의 발현도에 따라 가장 유사한 발현 패턴을 보이는 샘플들이 클러스터링 되도록 하는 방법이다. 이렇게 클러스터링 분석을 수행하여 예측유전자군의 발현 패턴에 따라 크게 두 가지 그룹으로 나눌 수 있음을 알 수 있다 (도 3 참고). 예측유전자군에 의해 분류된 두 개의 그룹은 카플란 메이어 (Kaplan Meier) 생존분석으로 측정한 결과 통계적으로 유의한 생존율 차이를 보였 다 (도 4 참고). 이는 예측유전자군이 간암수술 환자의 생존예측에 매우 유의적인 것임을 보여준다. 클러스터링 분석으로 얻어진 두 개의 그룹을 대상으로 교차평가 (cross validation)을 수행한 결과, 정확도가 96% 정도가 된다는 것을 확인하였다.After the liver cancer resection, the survival time of the patients was measured, and then used as a dependent variable. Cox regression analysis was performed. On the other hand, by using the gene expression measured by the microarray as an independent variable, the genes related to survival were sorted and sorted statistically. The top 156 genes were selected from these statistically aligned genes (FIG. 2). The genes are all known in sequence and are registered in GenBank hosted by the US NIH. Accordingly, the genes can be obtained from GenBank by entering an accession number, which is described in FIG. 2. After naming these genes as predictive gene groups, hierarchical clustering was performed (see FIG. 3). 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. This clustering analysis can be seen that can be divided into two groups according to the expression pattern of the predicted gene group (see Figure 3). The two groups classified by predictive gene group showed statistically significant survival differences as measured by Kaplan Meier survival analysis (see FIG. 4). This shows that the predictive gene group is very important for the survival prediction of liver cancer patients. As a result of performing cross validation on the two groups obtained by clustering analysis, it was confirmed that the accuracy was about 96%.

예측유전자군의 활용도를 평가하기 위해 신규 간암 절제수술을 받은 환자를 대상으로 예측 평가분석 (predicting analysis)을 수행하였다. 신규 간암환자로부터 동일한 방법으로 간암조직 37개를 절제한 후 마이크로어레이 분석을 실시한 후, 이미 선별한 예측유전자군에 대한 발현 패턴을 분석하여 생존기간의 예측을 평가하였다. 신규 샘플의 경우도 예측유전자군의 발현에 따라 크게 두 개의 그룹으로 분류됨을 확인하였으며 (도 5 참고), 이 두 그룹간의 생존율을 카플란 메이어 (Kaplan Meier) 생존분석으로 측정한 결과 통계적으로 유의한 생존율 차이를 보였다 (도 6 참고). 이는 예측유전자의 발현 패턴을 분석함으로써 신규 간암절제 수술을 받은 환자의 예후를 미리 예측할 수 있음을 보여주며, 이런 예측을 바탕으로 환자의 예후치료에 활용할 수 있을 것으로 판단한다.In order to evaluate the utility of predictive gene groups, predictive analysis was performed on patients undergoing resection of liver cancer. 37 liver cancer tissues were excised from new liver cancer patients in the same manner, followed by microarray analysis, and the expression patterns of the predicted gene groups that had been selected were analyzed. The new samples were also classified into two groups according to the expression of the predicted gene group (see FIG. 5). Survival rate between these two groups was measured by Kaplan Meier survival analysis. The difference was seen (see Figure 6). 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 it can be used for the prognosis of patients based on the prediction.

본 발명의 마커는 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 폴리뉴클레오티드로서, 도 2a에 기재된 50개의 폴리뉴클레오티드로 구성된 군으로부터 선택된 하나 이상의 폴리뉴클레오티드를 말하며, 바람직하게는 도 2a에 기재된 50개 폴리뉴클레오티드 세트를 포함한다. 즉, 50개 폴리뉴클레오티드 세트 전부를 이용하면 보다 효율적으로 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 것이다.The marker of the present invention is a polynucleotide capable of predicting the postoperative survival of a person who has undergone liver resection for liver cancer, and refers to one or more polynucleotides selected from the group consisting of 50 polynucleotides described in FIG. 2A, and preferably, FIG. 2A. And the 50 polynucleotide sets described. In other words, all 50 sets of polynucleotides can be used to more efficiently predict the postoperative survival of a person who has undergone liver resection for liver cancer.

또한, 본 발명의 마커는 도 2b 및 도 2c에 기재된 폴리뉴클레오티드로 구성 된 군으로부터 선택된 하나 이상의 폴리뉴클레오티드를 추가로 포함하며, 바람직하게는 도 2a, 도 2b 및 도 2c에 기재된 156개 폴리뉴클레오티드 세트를 포함한다. 즉, 156개 폴리뉴클레오티드 세트 전부를 이용하면 보다 효율적으로 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 것이다.In addition, the markers of the present invention further comprise one or more polynucleotides selected from the group consisting of the polynucleotides described in Figures 2b and 2c, preferably the set of 156 polynucleotides described in Figures 2a, 2b and 2c. It includes. In other words, all 156 sets of polynucleotides can be used to more efficiently predict the postoperative survival of a person who has undergone liver resection for liver cancer.

본 발명은 또한, 본 발명에 따른 마커를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 키트를 제공한다.The present invention also provides a kit comprising a marker according to the present invention for predicting survival after surgery of a person who has undergone liver resection for liver cancer.

본 발명의 키트는 또한, 도 2a, 도 2b 및 도 2c에 기재된 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드의 센스 및 안티센스 프라이머를 포함할 수 있다. 상기 폴리뉴클레오티드의 센스 및 안티센스 프라이머를 이용하여 PCR 증폭을 실시하여 원하는 생성물의 생성 여부를 통해 간암 환자의 생존 기간을 예측할 수 있는 것이다.Kits of the invention may also include sense and antisense primers of one or more polynucleotides selected from the group consisting of the polynucleotides described in FIGS. 2A, 2B, and 2C. PCR amplification is performed using the sense and antisense primers of the polynucleotides to predict survival of liver cancer patients through the generation of desired products.

본 발명의 키트는 또한, 도 2a, 도 2b 및 도 2c에 기재된 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드에 상보적인 프로브를 포함할 수 있다. 상기 폴리뉴클레오티드와 상보적인 프로브를 이용하여 혼성화를 실시하여, 혼성화 여부를 통해 간암 환자의 생존기간을 예측할 수 있는 것이다.Kits of the invention may also comprise probes complementary to one or more polynucleotides selected from the group consisting of the polynucleotides described in FIGS. 2A, 2B, and 2C. Hybridization may be performed using a probe complementary to the polynucleotide, thereby predicting survival of liver cancer patients through hybridization.

본 발명의 키트는 또한, 도 2a, 도 2b 및 도 2c에 기재된 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드에 의해 코딩되는 단백질을 인식하는 항체를 포함할 수 있다. 상기 키트는 상기 항체, 기질, 적당한 완충용액, 발색 효소 또는 형광물질로 표지된 2차 항체, 발색 기질 등을 포함할 수 있다. 상기에서 기질은 니트로셀룰로오스 막, 폴리비닐 수지로 합성된 96 웰 플레 이트, 폴리스티렌 수지로 합성된 96 웰 플레이트 및 유리로 된 슬라이드글라스 등이 이용될 수 있고, 발색효소는 퍼옥시다아제(peroxidase), 알칼라인 포스파타아제(Alkaline Phosphatase)가 사용될 수 있고, 형광물질은 FITC, RITC 등이 사용될 수 있고, 발색 기질액은 ABTS(2,2'-아지노-비스(3-에틸벤조티아졸린-6-설폰산)) 또는 OPD(o-페닐렌디아민), TMB(테트라메틸 벤지딘)가 사용될 수 있다.Kits of the invention may also include antibodies that recognize a protein encoded by one or more polynucleotides selected from the group consisting of the polynucleotides described in FIGS. 2A, 2B, and 2C. 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, and the chromase is peroxidase, alkaline. The phosphatase (Alkaline Phosphatase) can be used, the fluorescent material can be used FITC, RITC and the like, the colorant substrate liquid is ABTS (2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) )) Or OPD (o-phenylenediamine), TMB (tetramethyl benzidine) can be used.

본 발명의 상기 간암 환자 생존기간 예측용 폴리뉴클레오티드에 대한 항체는 각 유전자를 통상적인 방법에 따라 발현벡터에 클로닝하여 상기 마커 유전자에 의해 코딩되는 단백질을 얻고, 얻어진 단백질로부터 통상적인 방법에 의해 제조될 수 있다. 여기에는 상기 단백질에서 만들어질 수 있는 부분 펩타이드도 포함되며, 본 발명의 부분 펩타이드로는, 최소한 7개 아미노산, 바람직하게는 9개 아미노산, 더욱 바람직하게는 12개 이상의 아미노산을 포함한다. 본 발명의 항체의 형태는 특별히 제한되지 않으며 폴리클로날 항체, 모노클로날 항체 또는 항원 결합성을 갖는 것이면 그것의 일부도 본 발명의 항체에 포함되고 모든 면역 글로불린 항체가 포함된다. 나아가, 본 발명의 항체에는 인간화 항체 등의 특수 항체도 포함된다.The antibody to the polynucleotide for predicting the survival time of the liver cancer patient of the present invention can be produced by a conventional method from the obtained protein by cloning each gene into an expression vector according to a conventional method, to obtain a protein encoded by the marker gene. Can be. 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.

본 발명은 또한, 본 발명에 따른 마커를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 마이크로어레이를 제공한다. 본 발명의 마이크로어레이는 본 발명의 마커를 이용하여 당업계에서 통상적으로 사용되는 제조 방법에 의하여 용이하게 제조될 수 있다.The present invention also provides a microarray comprising a marker according to the present invention for predicting survival after surgery of a person who has undergone liver resection for liver cancer. 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.

본 발명은 또한, 간암 세포로부터 도 2a, 도 2b 및 도 2c에 기재된 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드의 발현 수 준을 측정하는 단계; 및The invention also includes measuring the expression level of at least one polynucleotide selected from the group consisting of polynucleotides described in FIGS. 2A, 2B and 2C from liver cancer cells; And

상기 측정된 발현 수준을 정상 세포의 발현 수준과 비교하여 간암 수술 환자의 생존기간을 예측하는 단계를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측하는 방법을 제공한다.Comprising the step of predicting the survival of liver cancer surgery patients by comparing the measured expression level with the expression level of normal cells, it provides a method for predicting the postoperative survival of people who have undergone liver resection for liver cancer.

상기 방법에서, 상기 폴리뉴클레오티드는 도 2a, 도 2b 및 도 2c에 기재된 156개 폴리뉴클레오티드 세트를 포함한다. 즉, 156개 폴리뉴클레오티드 세트 전부를 이용하면 보다 효율적으로 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 것이다.In this method, the polynucleotide comprises the 156 polynucleotide set described in Figures 2A, 2B and 2C. In other words, all 156 sets of polynucleotides can be used to more efficiently predict the postoperative survival of a person who has undergone liver resection for liver cancer.

이하, 실시예를 통하여 본 발명을 더욱 상세히 설명하기로 한다. 이들 실시예는 단지 본 발명을 예시하기 위한 것이므로, 본 발명의 범위가 이들 실시예에 의해 제한되는 것으로 해석되지는 않는다.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.

실시예Example 1:  One: 시험군Test group 및 시험조직 샘플의 선택 And selection of test tissue samples

시험용 표본은 원자력병원에서 간암 치료로 수술을 받은 73명의 환자로부터 수득하였다. 연구 목적으로 수술 표본과 임상병리학적 데이터를 사용하겠다는 동의를 환자로부터 받았다. 모든 경우에서 B형 간염 표면 항원-양성, HCV-음성이었다. 모든 조직은 외과적으로 적출되고 즉시 액체 질소에서 재빨리 냉각한 후 영하 80℃에서 저장하였다. RNA는 시험 매뉴얼 (Trizol, Gibco/BRL)에 따라 페놀/클로로포름 추출에 의해서 냉각된 조직으로부터 분리되었다. 총 RNA의 품질(quality)은 아가로즈 겔 전기영동 후 28S와 18S RNA 사이의 밴드 비율로부터 판단하였다.Test specimens were obtained from 73 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. In all cases hepatitis B surface antigen-positive, HCV-negative. 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.

실시예Example 2: cDNA  2: cDNA 마이크로어레이Microarray

실험에 사용한 마이크로어레이는 인간 cDNA 클론 총 14,080개로 구성되어 있다. 마이크로어레이 실험 방법은 3DNA 어레이 검출 키트 (Genisphere Inc. PA, USA)를 사용하여 제조사가 제시하는 방법에 따라 수행하였다. 간단히 말하면, 각각 실험용 RNA 10㎍과 일반적인 대조 태반 RNA 10㎍을 각각 2 시간 동안 역전사효소를 사용하여 역전사시키고 Cy5-dUTP와 Cy3-dUTP로 태깅하였다. 두 종류의 cDNA는 마이크로콘 칼럼 (Millipore, Bedford, MA)을 통과시켜 정제하였다. 대조 샘플과 실험용 샘플로부터 정제한 형광 cDNA는 ExpressHybTM 하이브리드 용액 (BD Biosciences Clontech, Palo Alto, CA, USA)과 혼합하여 65℃에서 하룻밤 동안 반응한 후 스캔어레이스캐너(PerkinElmer, Boston, MA, USA)를 사용하여 스캔하였다.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.). The fluorescent cDNA purified from the control sample and the experimental sample was mixed with ExpressHybTM hybrid solution (BD Biosciences Clontech, Palo Alto, Calif., USA) and reacted overnight at 65 ° C. Scan using.

실시예Example 3: 데이터 분석 및 예측유전자 선별 3: Data Analysis and Predictive Gene Screening

스캔한 이미지 파일로부터 IMAGENE 4.0 (Biodiscovery, Marina del Rey, CA, USA)를 이용하여 수치화된 데이터를 얻었고, 형광 강도 (intensity)와 스폿 위치를 고려하여(spatially dependent method) 표준화 (normalization)하였다. 적어도 샘플의 80% 이상에서 측정되지 않은 유전자는 차후 분석에서 배제하였다. 발현 비율은 클러스터 프로그램 (Cluster)의 사용으로 계층적으로 클러스터링하였으며, 트리뷰 (treeview) 프로그램을 사용하여 도식화하였다. 생존분석을 위해서 BRB ArrayTools 소프트웨어 (National Cancer Institute, USA)를 사용하였다. 생존에 관련된 예측유전자 군을 선별하기 위해 마이크로어레이에 부착되어 있는 유전자 프로브의 발현도를 개개의 독립변수로 설정한 뒤, 콕스회귀분석(Cox Regression Analysis)을 수행하였으며, 일변량분석 (univariate Analysis)과 다변량분석 (multivariate Analysis)을 이용하여 생존기간과 관련된 유전자를 추출하였다. 콕스회귀분석으로 얻어진 생존기간 예측유전자를 대상으로 클러스터링을 수행하여 유전자 발현 패턴에 따라 두 개의 군으로 분류하였으며, 이 두 개 군의 생존율 차이는 카플란 메이어 (Kaplan Meier) 방법을 사용하여 통계처리 하였다.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. BRB ArrayTools software (National Cancer Institute, USA) was used for survival analysis. In order to select a group of predictors related to survival, the expression level of the gene probe attached to the microarray was set as an independent variable, and then Cox Regression Analysis was performed, and univariate analysis was performed. Genes related to survival were extracted using multivariate analysis. The survival predictor genes obtained by Cox regression analysis were clustered and classified into two groups according to gene expression patterns. The difference in survival rates between the two groups was statistically analyzed using Kaplan Meier method.

실시예Example 4: 예측유전자에 의한 간암 절제술을 받은 환자의 생존기간 예측 4: Prediction of survival for patients undergoing hepatic cancer resection

콕스회귀분석에 의해 얻어진 예측유전자를 통계치의 순위별로 정렬을 하여 상위 유전자 50종을 선별하였으며 (도 2a), 그 다음 순위의 유전자 106종을 선별하였다 (도 2b 및 도 2c). 선별한 유전자를 언수퍼바이즈드 클러스터링 알고리즘(Unsupervised clustering algorithm) 방법에 의해 클러스터링하였다. 클러스터링 결과, 예측유전자들의 발현 패턴에 의해 샘플이 크게 두 개의 군으로 분류된다는 것을 확인하였다 (도 3). 각 군을 단기생존군과 장기생존군으로 명명하였다. 단기생존군과 장기생존군의 생존율 차이는 카플란 메이어 방법을 통해 확인하였다 (P<0.001, 도 4). 단기생존군과 장기생존군의 예측력을 평가하기 위하여 교차평가 (cross validation)를 수행하였다. 교차평가 수행결과, 전체의 예측정확도는 96% 이었으며, 각각의 경우 단기생존군의 예측정확도는 95.7%, 장기생존군의 예측정확도는 96.2%였다. 예측유전자의 임상적 적용을 위해서는 교차평가뿐만 아니라 독립적인 샘플의 예측과정이 필수적이다. 즉, 실험에 포함되지 않은 제3의 기관으로부터 얻어진 간암 환자의 생존기간을 예측할 수 있어야 진실한 의미의 임상적 적용이 가능하다 할 수 있다. 본 실험의 경우, 외부기관으로부터 확보한 간암절제조직 37개를 이용하여 예측유전자의 임상적 예측력을 평가하였다. 우선 교차평가와 같은 방법에 의해 실험에 이용한 샘플을 이용하여 예측유전자군에 의해 단기생존군과 장기생존군을 분류하였으며 (도 5), 이들의 유전자 발현 패턴을 이용하여 독립적인 신규 샘플을 두 개의 군 중 하나의 군에 배속시키는 방식을 사용하였다. 이 과정에 사용된 알고리즘은 BRB ArrayTools 소프트웨어에서 제공하는 방식을 사용하였다. 신규 암 조직의 예측 결과는 카플란 메이어 방법을 이용하여 확인하였다 (도 6). 결과에서 확인할 수 있듯이, 본 실험에 사용된 결과와 비슷한 수준의 예측 결과를 보여주고 있으며, 이는 예측유전자의 예측력이 신규 독립 환자의 생존 예측에 매우 유용하게 사용될 수 있음을 보여준다.The predicted genes obtained by Cox regression analysis were sorted by the rank of statistics, and the top 50 genes were selected (FIG. 2A), followed by the 106 rank genes (FIG. 2B and 2C). Selected genes were clustered by the Unsupervised clustering algorithm method. As a result of the clustering, it was confirmed that the sample was largely divided into two groups by the expression pattern of the predictor genes (FIG. 3). Each group was named short-term survival and long-term survival. The survival rate difference between the short-term survival group and the long-term survival group was confirmed by the Kaplan Meyer method (P <0.001, FIG. 4). Cross validation was performed to evaluate the predictive power of short-term and long-term survival groups. As a result of the cross-assessment, the overall prediction accuracy was 96%. In each case, the prediction accuracy of the short-term survival group was 95.7% and the long-term survival group was 96.2%. For the clinical application of predictive genes, not only cross-assessment but also prediction of independent samples is essential. In other words, it can be said that the clinical application of the true meaning is possible only when the survival time of the liver cancer patients obtained from the third institution not included in the experiment can be predicted. In this study, clinical predictive power of predictive genes was evaluated using 37 liver cancer resection tissues obtained from external institutions. First, the short-lived and long-lived groups were classified by the predictive gene group using the sample used in the experiment by the same method as the cross-assessment (FIG. 5), and two independent new samples were generated by using their gene expression patterns. A method of assigning to one of the groups was used. The algorithm used in this process uses the method provided by BRB ArrayTools software. Prediction of new cancer tissues was confirmed using the Kaplan Meyer method (FIG. 6). As can be seen from the results, the prediction results are similar to those used in this experiment, indicating that the predictive power of the predictive genes can be very useful for predicting survival of new independent patients.

본 발명에 따르면, 간암으로 진단받은 환자가 간 절제술을 받았을 경우, 간암조직을 이용하여 유전자의 발현도를 기준으로 환자의 수술 후 생존기간을 예측할 수 있도록 함으로써, 간암 수술을 받은 환자가 적절한 예후조치를 받는 것을 가능하게 했다는 것이다.According to the present invention, when a patient diagnosed with liver cancer has undergone liver resection, he or she can predict the survival after surgery of the patient based on the expression level of the gene using liver cancer tissue. It was made possible to receive.

본 발명은 간암 조직의 유전자 발현 패턴이라는 객관적인 요소를 바탕으로 간암환자를 분류함으로써 주관적 요소를 배제하였으며, 1-2 개 유전자의 발현도를 평가하는 것이 아니라 수십 개의 유전자의 발현 패턴을 동시에 측정하는 프로파일방식을 사용함으로써, 간암의 다양성을 고려할 수 있는 장치를 가지고 있어서 기존의 간암환자의 예후 예측보다 뛰어난 분류법으로 평가 받을 수 있을 것으로 판단한다. 한편, 기존의 임상분류와 본 예측유전자의 프로파일링 방식을 결합할 경우, 매우 우수한 간암환자의 분류가 가능하다는 것도 확인하였다. 따라서, 기존의 임 상 분류를 선호하는 의료진에게도 큰 거부감 없이 예측유전자에 의한 분류법이 이용될 수 있을 것으로 기대한다. 이런 예측유전자의 평가에 의해 단기생존군에 포함될 경우, 기존 방식의 치료보다 더 적극적인 치료를 시행할 수 있으며, 장기생존군에 포함이 될 경우 중복적인 치료를 최대한 줄여서 소모적인 의료비를 절감할 수 있는 효과를 기대할 수 있을 것이다.The present invention excludes subjective factors by classifying liver cancer patients based on objective factors called gene expression patterns of liver cancer tissues, and does not evaluate expression levels of 1-2 genes but measures profile patterns of dozens of genes simultaneously. By using, we have a device that can take into account the diversity of liver cancer and can be evaluated with better classification than the existing prognostic prediction of liver cancer patients. On the other hand, combining the existing clinical classification and the profiling method of this predictor gene, it was also confirmed that very good liver cancer classification is possible. Therefore, it is expected that classification based on predictive genes can be used even for medical staff who prefer the existing clinical classification. By evaluating these predictive genes, if they are included in the short-term survival group, they can be more aggressive than conventional treatments. If they are included in the long-term survival group, the redundant treatment can be minimized to reduce the cost of medical expenses. You can expect the effect.

따라서, 간암 절제수술을 받은 환자의 간암조직으로부터 예측유전자의 발현패턴인 프로파일링을 분석하여 생존기간을 예측할 수 있는 전략을 제시하여 환자의 예후치료에 적극적으로 활용을 할 수 있을 것으로 판단한다.Therefore, by analyzing profiling, which is an expression pattern of predictive genes, from liver cancer tissues of patients undergoing resection of liver cancer, we can suggest a strategy for predicting survival time, which can be used for prognostic treatment of patients.

Claims (11)

1) NM_017896 (Chromosome 20 open reading frame 11),1) NM_017896 (Chromosome 20 open reading frame 11), 2) BC008442 (Transmembrane 4 L six family member 1),2) BC008442 (Transmembrane 4 L six family member 1), 3) BI596851 (Hypothetical LOC400053),3) BI596851 (Hypothetical LOC400053), 4) NM_014624 (S100 calcium binding protein A6 (calcyclin)),4) NM_014624 (S100 calcium binding protein A6 (calcyclin)), 5) NM_000429 (Methionine adenosyltransferase I, alpha),5) NM_000429 (Methionine adenosyltransferase I, alpha), 6) AL162070 (Coronin, actin binding protein, 1C),6) AL162070 (Coronin, actin binding protein, 1C), 7) BI488771 (Lactate dehydrogenase D),7) BI488771 (Lactate dehydrogenase D), 8) AK026897 (Resistance to inhibitors of cholinesterase 8 homolog A (C. elegans)),8) AK026897 (Resistance to inhibitors of cholinesterase 8 homolog A (C. elegans)), 9) NM_001182 (Aldehyde dehydrogenase 7 family, member A1),9) NM_001182 (Aldehyde dehydrogenase 7 family, member A1), 10) NM_005500 (SUMO-1 activating enzyme subunit 1),10) NM_005500 (SUMO-1 activating enzyme subunit 1), 11) NM_001394 (Dual specificity phosphatase 4),11) NM_001394 (Dual specificity phosphatase 4), 12) NM_000238 (Potassium voltage-gated channel, subfamily H (eag-related), member 2),12) NM_000238 (Potassium voltage-gated channel, subfamily H (eag-related), member 2), 13) NM_002356 (Myristoylated alanine-rich protein kinase C substrate),13) NM_002356 (Myristoylated alanine-rich protein kinase C substrate), 14) NM_001316 (CSE1 chromosome segregation 1-like (yeast)),14) NM_001316 (CSE1 chromosome segregation 1-like (yeast)), 15) NM_001903 (Leucine rich repeat transmembrane neuronal 2),15) NM_001903 (Leucine rich repeat transmembrane neuronal 2), 16) AV761846 (Hypothetical protein MGC10744),16) AV761846 (Hypothetical protein MGC10744), 17) NM_001102 (Actinin, alpha 1),17) NM_001102 (Actinin, alpha 1), 18) NM_012229 (5'-nucleotidase, cytosolic II),18) NM_012229 (5'-nucleotidase, cytosolic II), 19) BF675222 (Small nuclear ribonucleoprotein polypeptide G),19) BF675222 (Small nuclear ribonucleoprotein polypeptide G), 20) NM_001153 (Annexin A4),20) NM_001153 (Annexin A4), 21) BF693017 (Desmoglein 2),21) BF693017 (Desmoglein 2), 22) NM_001511 (Chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha)),22) NM_001511 (Chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha)), 23) NM_005566 (Lactate dehydrogenase A),23) NM_005566 (Lactate dehydrogenase A), 24) NM_000820 (Growth arrest-specific 6),24) NM_000820 (Growth arrest-specific 6), 25) NM_031966 (Cyclin B1),25) NM_031966 (Cyclin B1), 26) NM_015416 (LETM1 domain containing 1),26) NM_015416 (LETM1 domain containing 1), 27) BE893802 (Hypothetical gene supported by AK091718),27) BE893802 (Hypothetical gene supported by AK091718), 28) AI082231 (Chromosome 3 open reading frame 10),28) AI082231 (Chromosome 3 open reading frame 10), 29) BC009238 (Tubulin, alpha 1 (testis specific)),29) BC009238 (Tubulin, alpha 1 (testis specific)), 30) NM_001521 (General transcription factor IIIC, polypeptide 2, beta 110kDa),30) NM_001521 (General transcription factor IIIC, polypeptide 2, beta 110kDa), 31) AU119959 (Taurine upregulated gene 1),31) AU119959 (Taurine upregulated gene 1), 32) NM_006819 (Stress-induced-phosphoprotein 1 (Hsp70/Hsp90-organizing protein)),32) NM_006819 (Stress-induced-phosphoprotein 1 (Hsp70 / Hsp90-organizing protein)), 33) NM_004896 (Vacuolar protein sorting 26 homolog A (yeast)),33) NM_004896 (Vacuolar protein sorting 26 homolog A (yeast)), 34) NM_018261 (SEC3-like 1 (S. cerevisiae)),34) NM_018261 (SEC3-like 1 (S. cerevisiae)), 35) NM_004766 (Coatomer protein complex, subunit beta 2 (beta prime)),35) NM_004766 (Coatomer protein complex, subunit beta 2 (beta prime)), 36) NM_002863 (Phosphorylase, glycogen; liver (Hers disease, glycogen storage disease type VI)),36) NM_002863 (Phosphorylase, glycogen; liver (Hers disease, glycogen storage disease type VI)), 37) BC002711 (Cell division cycle 42 (GTP binding protein, 25kDa)),37) BC002711 (Cell division cycle 42 (GTP binding protein, 25kDa)), 38) NM_016283 (TAF9 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 32kDa),38) NM_016283 (TAF9 RNA polymerase II, TATA box binding protein (TBP) -associated factor, 32kDa), 39) BG393525 (Myeloid/lymphoid or mixed-lineage leukemia 5 (trithorax homolog, Drosophila)),39) BG393525 (Myeloid / lymphoid or mixed-lineage leukemia 5 (trithorax homolog, Drosophila)), 40) NM_032704 (Tubulin alpha 6),40) NM_032704 (Tubulin alpha 6), 41) NM_003751 (Eukaryotic translation initiation factor 3, subunit 9 eta, 116kDa),41) NM_003751 (Eukaryotic translation initiation factor 3, subunit 9 eta, 116kDa), 42) NM_015136 (Stabilin 1),42) NM_015136 (Stabilin 1), 43) NM_002786 (Proteasome (prosome, macropain) subunit, alpha type, 1),43) NM_002786 (Proteasome (prosome, macropain) subunit, alpha type, 1), 44) NM_000895 (Leukotriene A4 hydrolase),44) NM_000895 (Leukotriene A4 hydrolase), 45) NM_000187 (Homogentisate 1,2-dioxygenase (homogentisate oxidase)),45) NM_000187 (Homogentisate 1,2-dioxygenase (homogentisate oxidase)), 46) AK026834 (CD58 antigen, (lymphocyte function-associated antigen 3)),46) AK026834 (CD58 antigen, (lymphocyte function-associated antigen 3)), 47) NM_021928 (Signal peptidase complex subunit 3 homolog (S. cerevisiae)),47) NM_021928 (Signal peptidase complex subunit 3 homolog (S. cerevisiae)), 48) NM_006496 (Guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 3),48) NM_006496 (Guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 3), 49) AF208850 (Protein tyrosine phosphatase type IVA, member 2), 및49) AF208850 (Protein tyrosine phosphatase type IVA, member 2), and 50) NM_000255 (Methylmalonyl Coenzyme A mutase)50) NM_000255 (Methylmalonyl Coenzyme A mutase) 의 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 마커.A marker capable of predicting survival after surgery of a person who has undergone liver resection for liver cancer, comprising one or more polynucleotides selected from the group consisting of polynucleotides. 제 1 항에 있어서,The method of claim 1, 1) 내지 50)번의 폴리뉴클레오티드 세트를 포함하는 것을 특징으로 하는 마커.A marker comprising 1) to 50) polynucleotide sets. 제 1 항에 있어서,The method of claim 1, 51) NM_004039 (Annexin A2),51) NM_004039 (Annexin A2), 52) NM_022821 (Elongation of very long chain fatty acids (FEN1/Elo2, SUR4/Elo3, yeast)-like 1),52) NM_022821 (Elongation of very long chain fatty acids (FEN1 / Elo2, SUR4 / Elo3, yeast) -like 1), 53) NM_000413 (Hydroxysteroid (17-beta) dehydrogenase 1),53) NM_000413 (Hydroxysteroid (17-beta) dehydrogenase 1), 54) AL582851 (Small EDRK-rich factor 2),54) AL582851 (Small EDRK-rich factor 2), 55) NM_003664 (Adaptor-related protein complex 3, beta 1 subunit),55) NM_003664 (Adaptor-related protein complex 3, beta 1 subunit), 56) NM_002595 (PCTAIRE protein kinase 2),56) NM_002595 (PCTAIRE protein kinase 2), 57) NM_023937 (Mitochondrial ribosomal protein L34),57) NM_023937 (Mitochondrial ribosomal protein L34), 58) H71227 (Phosphatidylinositol-4-phosphate 5-kinase, type II, beta),58) H71227 (Phosphatidylinositol-4-phosphate 5-kinase, type II, beta), 59) NM_021570 (BarH-like homeobox 1),59) NM_021570 (BarH-like homeobox 1), 60) NM_032906 (Hypothetical protein MGC14156),60) NM_032906 (Hypothetical protein MGC14156), 61) NM_032951 (Williams Beuren syndrome chromosome region 14),61) NM_032951 (Williams Beuren syndrome chromosome region 14), 62) NM_006096 (N-myc downstream regulated gene 1),62) NM_006096 (N-myc downstream regulated gene 1), 63) NM_009587 (Lectin, galactoside-binding, soluble, 9 (galectin 9)),63) NM_009587 (Lectin, galactoside-binding, soluble, 9 (galectin 9)), 64) NM_001357 (DEAH (Asp-Glu-Ala-His) box polypeptide 9),64) NM_001357 (DEAH (Asp-Glu-Ala-His) box polypeptide 9), 65) NM_003260 (Transducin-like enhancer of split 2 (E(sp1) homolog, 65) NM_003260 (Transducin-like enhancer of split 2 (E (sp1) homolog, Drosophila),Drosophila), 66) NM_012286 (Mortality factor 4 like 2),66) NM_012286 (Mortality factor 4 like 2), 67) AL512710 (SAR1a gene homolog 2 (S. cerevisiae)),67) AL512710 (SAR1a gene homolog 2 (S. cerevisiae)), 68) NM_006705 (Growth arrest and DNA-damage-inducible, gamma),68) NM_006705 (Growth arrest and DNA-damage-inducible, gamma), 69) NM_006999 (Polymerase (DNA directed) sigma),69) NM_006999 (Polymerase (DNA directed) sigma), 70) NM_032848 (Hypothetical protein FLJ14827),70) NM_032848 (Hypothetical protein FLJ14827), 71) NM_005826 (Heterogeneous nuclear ribonucleoprotein R),71) NM_005826 (Heterogeneous nuclear ribonucleoprotein R), 72) AL117595 (Kruppel-like factor 6),72) AL117595 (Kruppel-like factor 6), 73) NM_004514 (Forkhead box K2),73) NM_004514 (Forkhead box K2), 74) NM_001288 (Chloride intracellular channel 1),74) NM_001288 (Chloride intracellular channel 1), 75) NM_024057 (Nucleoporin 37kDa),75) NM_024057 (Nucleoporin 37kDa), 76) AW021507 (P300/CBP-associated factor),76) AW021507 (P300 / CBP-associated factor), 77) NM_014597 (Estrogen receptor binding protein),77) NM_014597 (Estrogen receptor binding protein), 78) BC010082 (Ethanolamine kinase 2),78) BC010082 (Ethanolamine kinase 2), 79) NM_002690 (Polymerase (DNA directed), beta),79) NM_002690 (Polymerase (DNA directed), beta), 80) NM_016045 (Chromosome 20 open reading frame 45),80) NM_016045 (Chromosome 20 open reading frame 45), 81) NM_004427 (Polyhomeotic-like 2 (Drosophila)),81) NM_004427 (Polyhomeotic-like 2 (Drosophila)), 82) NM_031449 (Hypothetical protein DKFZp761I2123),82) NM_031449 (Hypothetical protein DKFZp761I2123), 83) NM_000942 (Peptidylprolyl isomerase B (cyclophilin B)),83) NM_000942 (Peptidylprolyl isomerase B (cyclophilin B)), 84) AB058697 (Hypothetical protein FLJ10719),84) AB058697 (Hypothetical protein FLJ10719), 85) AI149362 (AF4/FMR2 family, member 4),85) AI149362 (AF4 / FMR2 family, member 4), 86) AW859966 (Similar to hypothetical protein B230397C21),86) AW859966 (Similar to hypothetical protein B230397C21), 87) NM_031899 (Golgi reassembly stacking protein 1, 65kDa),87) NM_031899 (Golgi reassembly stacking protein 1, 65kDa), 88) AK022820 (Family with sequence similarity 33, member A),88) AK022820 (Family with sequence similarity 33, member A), 89) NM_006367 (CAP, adenylate cyclase-associated protein 1 (yeast)),89) NM_006367 (CAP, adenylate cyclase-associated protein 1 (yeast)), 90) AL049265 (Interleukin 6 signal transducer (gp130, oncostatin M receptor)),90) AL049265 (Interleukin 6 signal transducer (gp130, oncostatin M receptor)), 91) NM_014730 (KIAA0152),91) NM_014730 (KIAA0152), 92) AK023769 (Zinc finger protein 552),92) AK023769 (Zinc finger protein 552), 93) NM_001038 (Sodium channel, nonvoltage-gated 1 alpha),93) NM_001038 (Sodium channel, nonvoltage-gated 1 alpha), 94) NM_001172 (Vesicle transport through interaction with t-SNAREs homolog 1B (yeast)),94) NM_001172 (Vesicle transport through interaction with t-SNAREs homolog 1B (yeast)), 95) NM_002306 (Lectin, galactoside-binding, soluble, 3 (galectin 3)),95) NM_002306 (Lectin, galactoside-binding, soluble, 3 (galectin 3)), 96) NM_005410 (Selenoprotein P, plasma, 1),96) NM_005410 (Selenoprotein P, plasma, 1), 97) NM_007043 (HIV-1 rev binding protein 2),97) NM_007043 (HIV-1 rev binding protein 2), 98) NM_020474 (UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 1),98) NM_020474 (UDP-N-acetyl-alpha-D-galactosamine: polypeptide N-acetylgalactosaminyltransferase 1), 99) NM_006842 (Splicing factor 3b, subunit 2, 145kDa),99) NM_006842 (Splicing factor 3b, subunit 2, 145 kDa), 100) NM_000445 (Plectin 1, intermediate filament binding protein 500kDa),100) NM_000445 (Plectin 1, intermediate filament binding protein 500kDa), 101) NM_013341 (GTP-binding protein PTD004),101) NM_013341 (GTP-binding protein PTD004), 102) AK021884 (Hypothetical protein FLJ11822),102) AK021884 (Hypothetical protein FLJ11822), 103) NM_001607 (Acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiolase)),103) NM_001607 (Acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiolase)), 104) NM_003380 (Vimentin),104) NM_003380 (Vimentin), 105) NM_016824 (Adducin 3 (gamma)),105) NM_016824 (Adducin 3 (gamma)), 106) AK022819 (Chromosome 9 open reading frame 25),106) AK022819 (Chromosome 9 open reading frame 25), 107) NM_004168 (Succinate dehydrogenase complex, subunit A, flavoprotein (Fp)),107) NM_004168 (Succinate dehydrogenase complex, subunit A, flavoprotein (Fp)), 108) BI825434 (Chromosome 1 open reading frame 102),108) BI825434 (Chromosome 1 open reading frame 102), 109) AK027646 (Thioredoxin domain containing 11),109) AK027646 (Thioredoxin domain containing 11), 110) NM_005030 (Polo-like kinase 1 (Drosophila)),110) NM_005030 (Polo-like kinase 1 (Drosophila)), 111) BE787020 (Similar to common salivary protein 1),111) BE787020 (Similar to common salivary protein 1), 112) BE252211 (Chromobox homolog 5 (HP1 alpha homolog, Drosophila)),112) BE252211 (Chromobox homolog 5 (HP1 alpha homolog, Drosophila)), 113) BE875638 (Acyl-Coenzyme A dehydrogenase, short/branched chain),113) BE875638 (Acyl-Coenzyme A dehydrogenase, short / branched chain), 114) NM_002966 (S100 calcium binding protein A10 (annexin II ligand, calpactin I, light polypeptide (p11))),114) NM_002966 (S100 calcium binding protein A10 (annexin II ligand, calpactin I, light polypeptide (p11))), 115) AB040890 (Phosphatidylinositol transfer protein, membrane-associated 2),115) AB040890 (Phosphatidylinositol transfer protein, membrane-associated 2), 116) NM_017931 (Hypothetical protein FLJ20699),116) NM_017931 (Hypothetical protein FLJ20699), 117) NM_004992 (Methyl CpG binding protein 2 (Rett syndrome)),117) NM_004992 (Methyl CpG binding protein 2 (Rett syndrome)), 118) NM_024526 (EPS8-like 3),118) NM_024526 (EPS8-like 3), 119) NM_004808 (N-myristoyltransferase 2),119) NM_004808 (N-myristoyltransferase 2), 120) NM_000308 (Protective protein for beta-galactosidase (galactosialidosis)),120) NM_000308 (Protective protein for beta-galactosidase (galactosialidosis)), 121) NM_024038 (Hypothetical protein MGC2803),121) NM_024038 (Hypothetical protein MGC2803), 122) NM_000157 (Glucosidase, beta; acid (includes glucosylceramidase)),122) NM_000157 (Glucosidase, beta; acid (includes glucosylceramidase)), 123) AI286307 (Transcribed locus),123) AI286307 (Transcribed locus), 124) BE858787 (Phosphatidic acid phosphatase type 2 domain containing 1B),124) BE858787 (Phosphatidic acid phosphatase type 2 domain containing 1B), 125) NM_000302 (Procollagen-lysine 1, 2-oxoglutarate 5-dioxygenase 1),125) NM_000302 (Procollagen-lysine 1, 2-oxoglutarate 5-dioxygenase 1), 126) AW022579 (Fibronectin type III domain containing 3B),126) AW022579 (Fibronectin type III domain containing 3B), 127) NM_002628 (Profilin 2),127) NM_002628 (Profilin 2), 128) NM_018380 (DEAD (Asp-Glu-Ala-Asp) box polypeptide 28),128) NM_018380 (DEAD (Asp-Glu-Ala-Asp) box polypeptide 28), 129) AF205218 (Influenza virus NS1A binding protein),129) AF205218 (Influenza virus NS1A binding protein), 130) NM_005228 (Epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)),130) NM_005228 (Epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)), 131) AK026263 (BAI1-associated protein 2-like 2),131) AK026263 (BAI1-associated protein 2-like 2), 132) NM_004772 (Chromosome 5 open reading frame 13),132) NM_004772 (Chromosome 5 open reading frame 13), 133) AK000296 (Hypothetical protein FLJ11029),133) AK000296 (Hypothetical protein FLJ11029), 134) AI741611 (F-box protein 31),134) AI741611 (F-box protein 31), 135) NM_012151 (Coagulation factor VIII-associated (intronic transcript) 1),135) NM_012151 (Coagulation factor VIII-associated (intronic transcript) 1), 136) NM_005165 (Aldolase C, fructose-bisphosphate),136) NM_005165 (Aldolase C, fructose-bisphosphate), 137) NM_012100 (Aspartyl aminopeptidase),137) NM_012100 (Aspartyl aminopeptidase), 138) NM_002629 (Phosphoglycerate mutase 1 (brain)),138) NM_002629 (Phosphoglycerate mutase 1 (brain)), 139) NM_006263 (Proteasome (prosome, macropain) activator subunit 1 (PA28 alpha)),139) NM_006263 (Proteasome (prosome, macropain) activator subunit 1 (PA28 alpha)), 140) NM_006247 (Protein phosphatase 5, catalytic subunit),140) NM_006247 (Protein phosphatase 5, catalytic subunit), 141) NM_022736 (Major facilitator superfamily domain containing 1),141) NM_022736 (Major facilitator superfamily domain containing 1), 142) NM_016185 (Hematological and neurological expressed 1),142) NM_016185 (Hematological and neurological expressed 1), 143) NM_015904 (Eukaryotic translation initiation factor 5B),143) NM_015904 (Eukaryotic translation initiation factor 5B), 144) D26488 (WD repeat domain 43),144) D26488 (WD repeat domain 43), 145) W74586 (Ras homolog gene family, member B),145) W74586 (Ras homolog gene family, member B), 146) AI991240 (SFT2 domain containing 3),146) AI991240 (SFT2 domain containing 3), 147) NM_031431 (Component of oligomeric golgi complex 3),147) NM_031431 (Component of oligomeric golgi complex 3), 148) AL162068 (60S ribosomal protein L6 (RPL6A)),148) AL162068 (60S ribosomal protein L6 (RPL6A)), 149) NM_006408 (Anterior gradient 2 homolog (Xenopus laevis)),149) NM_006408 (Anterior gradient 2 homolog (Xenopus laevis)), 150) NM_001537 (Heat shock factor binding protein 1),150) NM_001537 (Heat shock factor binding protein 1), 151) NM_005620 (S100 calcium binding protein A11 (calgizzarin)),151) NM_005620 (S100 calcium binding protein A11 (calgizzarin)), 152) NM_003633 (Ectodermal-neural cortex (with BTB-like domain)),152) NM_003633 (Ectodermal-neural cortex (with BTB-like domain)), 153) NM_001428 (Enolase 1, (alpha)),153) NM_001428 (Enolase 1, (alpha)), 154) NM_003364 (Uridine phosphorylase 1),154) NM_003364 (Uridine phosphorylase 1), 155) NM_002907 (RecQ protein-like (DNA helicase Q1-like)) 및155) NM_002907 (RecQ protein-like (DNA helicase Q1-like)) and 156) NM_023005 (Bromodomain adjacent to zinc finger domain, 1B)156) NM_023005 (Bromodomain adjacent to zinc finger domain, 1B) 의 폴리뉴클레오티드를 포함하는 군으로부터 선택되는 하나 이상의 폴리뉴클 레오티드를 추가로 포함하는 것을 특징으로 하는 마커.The marker further comprises one or more polynucleotides selected from the group comprising polynucleotides of. 제 3 항에 있어서,The method of claim 3, wherein 1) 내지 156)번의 폴리뉴클레오티드 세트를 포함하는 것을 특징으로 하는 마커.A marker comprising the polynucleotide set of 1) to 156). 제 1 항 내지 제 4 항 중 어느 한 항에 따른 마커를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 키트.A kit capable of predicting survival after surgery of a person who has undergone liver resection for liver cancer, comprising the marker according to any one of claims 1 to 4. 제 5 항에 있어서,The method of claim 5, 상기 1) 내지 156)번의 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드의 센스 및 안티센스 프라이머를 포함하는 것을 특징으로 하는 키트.And a sense and antisense primer of at least one polynucleotide selected from the group consisting of polynucleotides 1) to 156). 제 5 항에 있어서,The method of claim 5, 상기 1) 내지 156)번의 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드에 상보적인 프로브를 포함하는 것을 특징으로 하는 키트.And a probe complementary to at least one polynucleotide selected from the group consisting of polynucleotides 1) to 156). 제 5 항에 있어서,The method of claim 5, 상기 1) 내지 156)번의 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드에 의해 코딩되는 단백질을 인식하는 항체를 포함하는 것을 특징으로 하는 키트.And a antibody that recognizes a protein encoded by at least one polynucleotide selected from the group consisting of polynucleotides 1) to 156). 제 1 항 내지 제 4 항 중 어느 한 항에 따른 마커를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측할 수 있는 마이크로어레이.A microarray capable of predicting survival after surgery of a person who has undergone liver resection for liver cancer, comprising the marker according to any one of claims 1 to 4. 간암 세포로부터 상기 1) 내지 156)번의 폴리뉴클레오티드로 구성된 군으로부터 선택되는 하나 이상의 폴리뉴클레오티드의 발현 수준을 측정하는 단계; 및Measuring the expression level of at least one polynucleotide selected from the group consisting of polynucleotides 1) to 156) from liver cancer cells; And 상기 측정된 발현 수준을 정상 세포의 발현 수준과 비교하여 간암 수술 환자의 생존기간을 예측하는 단계를 포함하는, 간암으로 간 절제술을 받은 사람의 수술 후 생존기간을 예측하는 방법.Comparing the measured expression level with the expression level of normal cells to predict the survival time of a liver cancer surgery patient, the method of predicting the survival time after surgery of a person who has undergone liver resection for liver cancer. 제 10 항에 있어서,The method of claim 10, 상기 폴리뉴클레오티드는 1) 내지 156)번의 폴리뉴클레오티드 세트인 것을 특징으로 하는 방법.The polynucleotide is a set of polynucleotides 1) to 156).
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