KR101504818B1 - Novel system for predicting prognosis of gastric cancer - Google Patents

Abstract

The present invention relates to a novel prognostic prediction system capable of predicting the prognosis of stomach cancer, and more particularly, it is possible to predict the clinical outcome after surgical resection of stomach cancer through comparative analysis of gene expression.

Description

[0001] The present invention relates to a novel system for predicting prognosis of gastric cancer,

The present invention relates to a novel prognostic prediction system capable of predicting the prognosis of gastric cancer through comparative gene expression analysis.

Gastric adenocarcinoma is the fourth most common cancer in the world, the second most common cause of cancer death in 2000 (700,349 deaths). Is considered a single heterogeneous disorder with several epidemiological and histopathologic features. Gastric cancer treatment is based mainly on clinical parameters such as tumor, node, metastasis (TNM) staging, which determines whether patients should be treated surgically or by surgery and chemotherapy. Unlike breast cancer and colorectal cancer, stomach cancer differs markedly from stage 1 to stage 4 according to the TNM stage system. That is, the 5-year survival rate is more than 90% in case 1 and less than 20% in case 4. Thus, the prognosis of the TNM staging system is very good [7]. Ann Surg Oncol 2010; 17: 3077-3079]. Gastric cancer based on the above-described staging system can be divided into Early Gastric Cancer, Locally Advanced Gastric Cancer, Locally Advanced Invasive Gastric Cancer, and Metastatic Gastric Cancer .

Although surgery is the main treatment for viable gastric cancer, recurrence rates are high in advanced cases. In order to prevent recurrence and to improve the prognosis of patients with gastric cancer, chemotherapy and chemotherapy including chemotherapy was introduced. However, although these treatment modalities improve general clinical outcomes in patients, the clinicopathologic heterogeneity of the tumors and the different outcomes of patients in the same stage have limitations in predicting the task of adjuvant chemotherapy, There is a lack of optimal access to.

Despite recent advances, challenges remain for cancer treatment to personalize tumor therapy to target specific therapeutic regimens for distinctly different tumor types and ultimately maximize outcome. Therefore, tests that simultaneously provide predictive information about patient response to various treatment options are needed.

1. Korean Patent Publication No. 2007-0022694, February 27, 2007 2. Korean Patent Publication No. 2010-0072283, June 30, 2010

1. Journal of clinical oncology, vol. 23 (29), pp.7286-7295, 2005.10.10

It is an object of the present invention to provide a new prognostic prediction system based on a gene expression prognostic index or risk score that affects the clinical outcome of all gastric cancer patients not related to the TNM stage.

In order to achieve the above object, the present invention provides a method for determining the expression level of an RNA transcript of CSK, CD8A and CHEK2 in a biological sample comprising cancer cells obtained from a subject; And calculating a risk score (RS, Risk Score) and a RS percentage (RS (%)) of the biological sample based on the expression level of the RNA transcript determined in the step and determining a prognosis according to the RS (% A method for predicting a prognosis in a subject diagnosed with gastric cancer.

The present invention also relates to a method for detecting the expression level of an RNA transcript of CSK, CD8A and CHEK2 in a biological sample comprising cancer cells obtained from a subject; And determining that the increased expression of the transcript is an increase in the likelihood of a positive clinical outcome.

This prognostic prediction method may be to predict the clinical outcome after surgical resection of the entire gastric cancer that is not related to the TNM stage.

The present invention also relates to a computer-readable medium having recorded thereon a program for performing prognostic prediction of gastric cancer, comprising: determining the degree of expression of an RNA transcript of CSK, CD8A and CHEK2 in a nucleic acid sample obtained from a patient; (RS) and a RS percentage (RS) of the biological sample based on the expression level of the RNA determined in the step (a), and calculating the RS (%) in terms of overall survival (OS) ) Are recorded in a computer-readable recording medium that records a program for causing a computer to classify a high-risk group as having a set value of 75% or more, an intermediate-risk group as 25% to less than 75%, or a low- Thereby providing a recording medium.

The recording medium may be to predict the clinical outcome after surgical resection of whole gastric cancer which is not related to the TNM stage.

The present invention establishes a prediction model in terms of overall survival rate for all gastric cancer patients without regard to TNM stage, and then determines the expression level of RNA transcript that influences statistically significant survival, The clinical outcome after gastrectomy with stomach cancer surgery can be predicted.

Figure 1 shows the results of 520 gastric cancer stage at Yonsei University Severance Hospital between 2001 and 2004.
FIG. 2 shows the result of performing a standardization work on a reference gene.
FIG. 3 shows the survival analysis results of good and bad prognostic groups according to RNA transcript expression.
FIG. 4 shows the survival risk prediction result according to the PCA analysis using the BRB-Array Tools.
5 shows the results of verifying the accuracy of the prognosis model of the present invention.
6 shows survival analysis results according to the risk score (RS (%)) of gastric cancer patients.

Hereinafter, the structure of the present invention will be described in detail.

The present invention relates to a method for determining the expression level of an RNA transcript of CSK, CD8A and CHEK2 in a biological sample comprising cancer cells obtained from a subject; And calculating a risk score (RS, Risk Score) and a RS percentage (RS (%)) of the biological sample based on the expression level of the RNA transcript determined in the step and determining a prognosis according to the RS (% To a method for predicting a prognosis in a subject diagnosed with gastric cancer.

The method of predicting the prognosis according to the present invention was performed by selecting a gene group strongly associated with the prognosis of gastric cancer patients and a gene group strongly expressing the tumor biologic characteristics and performing qPCR to determine the gene having a statistical significance (p <0.001) in the Cox regression analysis (RS, Risk Score) and the RS percentage (RS (%)) according to the following equations (1) and (2) by multiplying the gene expression value by the hazard ratio of the genes, ) Were classified as high-risk, 25% to less than 75%, intermediate, and less than 25% of RS (%) in the overall survival (OS) It is characterized by being able to predict the prognosis in a subject diagnosed with gastric cancer.

The RS and RS (%) can be calculated according to the following equations (1) and (2): &lt; EMI ID =

[Equation 1]

RS = HR ₁ * normLogTransValue ₁ + HR ₂ * normLogTransValue ₂ + ... + HR _n * normLogTransValue _n

&Quot; (2) &quot;

RS (%) = 100 × (RS minimum value of the biological sample) / (RS maximum value of the population - RS minimum value of the population)

In this formula,

HR _n represents the hazard ratio of the n-th RNA transcript and is converted to -1 / HR _n when HR _n is less than 1,

normLogTransValue _n is a value associated with the expression of the RNA transcript and is a value obtained by varying the scale around the median based on the total value of the gene,

The population means a certain number of groups having a whole gastric cancer unrelated to the TNM stage, and the constant number is an arbitrary integer such that the RS maximum value and the minimum value can be calculated.

The number of the population is not particularly limited. In one embodiment, 520 whole gastric cancer tissues irrespective of TNM stage were used as a population.

As used herein, the term Hazard Ratio (HR) refers to a coefficient that reflects the contribution of a cancer to progression, relapse, or therapy response. Risk factors can be derived by various statistical techniques. The risk factor, HR value, may be determined in various statistical models, for example, in a multivariate Cox proportional hazards regression analysis. In one embodiment, when the HR value is used in the PI equation, the HR value is used as it is when the HR value is equal to or greater than 1, and -1 / HR value is used when the HR value is less than 1.

In the above expression, the expression value of the term RNA transcript refers to a value related to the expression of an individual gene, that is, an RNA transcript. The value can be determined using various statistical means known in the art. For example, the expression value can be obtained by transforming p value measured by Cox regression analysis into log2 function value and then quantile normalization. The expression value used in Equation (1) is a value obtained by changing the scale around the intermediate value with respect to the total value of the corresponding gene.

According to one embodiment, the RS may determine as follows:

RS = - CSK x 1.317 - CD8A x 1.18 - CHEK2 x 1.26

The RS calculated according to Equation (1) can be expressed as RS (%) according to Equation (2). When the value determined in the above equation is converted into a ranking in the population, a sample having the RS (%) of 75% or more in terms of overall survival (OS) is referred to as a high-risk group, and in the case of 25% , And less than 25%. The high-risk group can be judged as a bad prognosis and the low-risk group as a good prognosis. That is, a sample with a RS (%) value of 75% or higher means that the overall survival rate is low for more than 3 years, more than 6 years, more than 10 years for high risk group, more than 3 years for low risk group less than 25% This means that the overall survival rate is high for more than 10 years. The term, a good prognosis, can be expressed as an increase in the likelihood of a positive clinical outcome of a clinical outcome, and a bad prognosis can be expressed as a decrease in the likelihood of a positive clinical outcome of a clinical outcome.

This method may be useful for predicting clinical outcome after surgical resection of whole gastric cancer, regardless of TNM stage.

The present invention also relates to a method for detecting the expression level of an RNA transcript of CSK, CD8A and CHEK2 in a biological sample comprising cancer cells obtained from a subject; And determining that the increased expression of the transcript is an increase in the likelihood of a positive clinical outcome.

The method may be a qPCR-based method.

The expression level may be one that is normalized to the level of expression of one or more RNA transcripts.

The clinical outcome may be expressed in terms of Overall Survival (OS).

This method can predict the prognosis by measuring the expression level of the entire RNA transcript and analyzing the expression increase to judge the increase or decrease in the possibility of positive clinical outcome.

This method may be useful for predicting clinical outcome after surgical resection of whole gastric cancer, regardless of TNM stage.

The present invention also relates to a computer-readable medium having recorded thereon a program for performing prognostic prediction of gastric cancer, comprising: determining the degree of expression of an RNA transcript of CSK, CD8A and CHEK2 in a nucleic acid sample obtained from a patient; (RS), a risk score (RS), and a RS percentage (RS) of the sample based on the degree of expression of the RNA determined in the step, and calculating the RS (%) in terms of overall survival (OS) A computer readable recording of a program that causes a computer to perform the steps of classifying a high-risk group as having a set-value range of 75% or more, an intermediate-risk group as 25% to less than 75%, or a low-risk group as 25% Media.

The recording medium may be useful for predicting clinical outcome after surgical resection of whole stomach cancer independent of TNM stage.

The RS and RS (%) can be calculated according to Equations (1) and (2).

The recording medium is judged to be a high-risk group when the set value range of RS (%) is more than 75% in terms of overall survival (OS), an intermediate risk group when 25% or more to less than 75% can do. That is, a sample with a RS (%) value of 75% or more is considered to have a low overall survival rate for a period of 3 years, 6 years, 10 years or more in a high-risk group, Or more, and the overall survival rate is higher than 10 years. The term, a good prognosis, can be expressed as an increase in the likelihood of a positive clinical outcome of a clinical outcome, and a bad prognosis can be expressed as a decrease in the likelihood of a positive clinical outcome of a clinical outcome.

Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of skill in the art. The present invention is not limited in any way to the methods and materials described. For purposes of the present invention, the following terms are defined below.

The term "polynucleotide" generally refers to any polyribonucleotide or polydeoxyribonucleotide and can be, for example, a modified or non-modified RNA or DNA. As used herein, "polynucleotide" specifically includes cDNA.

The term "oligonucleotide" refers to a relatively short polynucleotide, including but not limited to one-strand deoxyribonucleotide, one- or two-strand ribonucleotides, RNA: DNA hybrids and two-stranded DNA. Oligonucleotides, such as one-stranded DNA probe oligonucleotides, are often synthesized, for example, by chemical methods using commercially available automated oligonucleotide synthesizers. However, oligonucleotides can be produced by various other methods, including in vitro recombinant DNA-mediated techniques, and by DNA expression in cells and organisms.

The term "differentially expressed gene" or "differential gene expression" refers to a gene that is activated at a higher or lower level than a cancerous subject, such as a stomach cancer, relative to the expression of a normal or control subject. It also includes genes that are activated at higher or lower levels in other stages of the same disease. The differentially expressed genes may be activated or inhibited at the nucleic acid or protein level, or they may undergo different splicing resulting in different polypeptide products. This difference can be demonstrated, for example, by changes in the mRNA level, surface expression, secretion or other distribution of the polypeptide. For the purposes of the present invention, "differential gene expression" means about 1.5 times, at least about 4 times, at least about 6 times, at least about 5 times, at least about 5 times, at least about 5 times, It is considered to be present when there is a difference of about 10 times or more.

The term "normalized " with respect to a gene transcript or gene expression product refers to the level of a transcript or gene expression product relative to an average level of a transcript / product of a set of reference genes, ("Housekeeping genes"), or a reference gene refers to the whole of the genes tested. In the latter case, it is generally referred to as "global normalization ", where it is important that the total number of genes tested is relatively large, preferably over 50. Specifically, the term &quot; standardized &quot; for an RNA transcript refers to the transcription level relative to the average of transcription levels of a set of reference genes.

The terms "expression threshold" and "defined expression threshold" are used interchangeably, and at this level above the level of the gene or gene product the gene or gene product is used as a predictive marker for the patient response. Thresholds are typically defined experimentally from clinical studies. Expression thresholds can be selected for maximum sensitivity, or for maximum selectivity (e.g., to select only responders for a drug), or minimum error.

The term "gene amplification" refers to the process by which multiple copies of a gene or gene fragment are formed in a particular cell or cell line. The replicated region (the elongation of amplified DNA) is often referred to as "amplicon. &Quot; Often, the amount of mRNA produced, or gene expression, also increases in proportion to the number of copies made of a particular gene.

As used herein, the term "prognosis" is used herein to refer to the prediction of the likelihood of death (including recurrence, metastatic spread, and drug resistance) of a neoplastic disease such as cancer or gastric cancer. The term "prediction" is used herein to refer to the likelihood that a patient will survive a specific period of time without cancer recurrence following surgical removal of a major tumor. Such prediction can be clinically used to determine treatment by selecting the most appropriate treatment technique for any particular patient. Such a prediction is a valuable tool in predicting whether a patient is likely to respond favorably to therapeutic regimens, such as surgical procedures, or whether long-term survival of a patient is possible after the end of surgery.

As used herein, "prolonged" survival is used herein to refer to survival of 3 years or more, more preferably 6 years or more, and most preferably 10 years or more after surgery or other treatment.

Unless otherwise indicated, the present invention may be practiced using conventional techniques of molecular biology (including recombinant techniques), microbiology, cell biology, and biochemistry.

1. Profiling of gene expression profile

Methods for generating gene expression profiles include methods based on hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, and methods based on proteomics. Methods for quantifying mRNA expression, for example, include northern blotting and in situ hybridization; RNAse protection assays; And PCR-based methods, such as reverse transcription polymerase chain reaction (RT-PCR). Alternatively, antibodies capable of recognizing two specific strands including two DNA strands, two RNA strands, and two DNA-RNA hybrid strands or two DNA-protein strands can be used. Representative methods for sequencing-based gene expression analysis include gene expression analysis by Serial Analysis of Gene Expression (SAGE) and massively parallel signature sequencing (MPSS).

2. PCR-Based Gene Expression Profiling Methods

a. Reverse transcriptase PCR (RT-PCR)

One of the most sensitive and most flexible quantitative PCR-based gene expression profile generation methods is RT-PCR, which compares mRNA levels in different sample populations in normal and tumor tissues with or without drug therapy Can be used to characterize gene expression patterns, identify closely related mRNAs, and analyze RNA structures.

The first step is the isolation of mRNA from the target sample. mRNA can be extracted from, for example, frozen or stored paraffin-embedded and fixed (e.g., formalin-fixed) tissue samples.

General methods for mRNA extraction are well known in the art, and methods for RNA extraction from paraffin-embedded tissue are described in Rupp and Locker, Lab Invest . 56: A67 (1987)] and [De Andres et al ., BioTechniques 18: 42044 (1995)). In particular, RNA isolation can be performed according to the manufacturer &apos; s instructions using commercial manufacturers, for example, purification kits from Qiagen, buffer sets and proteases. Commercially available RNA isolation kit is different from the master Poor (MasterPure) ^TM Complete DNA and RNA Purification Kit (epi cent LES (EPICENTRE) ^®, Madison, WI), and Paraffin Block (Paraffin Block) RNA isolation kit (aembi-one, Inc. (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumors can be isolated, for example, by cesium chloride density gradient centrifugation.

Since RNA can not be used as a template for PCR, the first step in creating a gene expression profile by RT-PCR is reverse transcription into the RNA template cDNA, followed by exponential amplification into its PCR reaction. The reverse transcriptase can be used mainly in avian myeloproliferative viral reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically an initial antigen stimulation using specific primers, random hexamers, or oligo-dT primers, depending on the environment and goals of expression profile creation. For example, the extracted RNA can be reverse-transcribed according to the manufacturer's instructions using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA). The derived cDNA can then be used as a template in subsequent PCR reactions.

Although the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, it typically uses Taq DNA polymerase, Taq DNA polymerase has 5'-3 'nuclease activity, while 3'- 5 'proofreading endonuclease activity is deficient. Thus, Takman &lt; ( ^R) &gt; PCR typically utilizes 5 &apos; -nuclease activity to hybridize a hybridization probe bound to its target ampicillin of a Taq or Tth polymerase, Enzymes can be used. Two oligonucleotide primers are used to generate a representative amplicon for the PCR reaction. The third oligonucleotide or probe is designed to detect the nucleotide sequence located between the two PCR primers. The probe is non-extensible by the Taq DNA polymerase enzyme and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is cleared by the quencher dye when the two dyes are located close together, such as when they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resulting probe fragments are dissociated in solution and the signal from the released reporter dye has no effect of eliminating the second fluorophore. One molecule of the reporter dye is released from each of the synthesized new molecules, and the detection of the non-erased reporter dye provides a basis for quantitative interpretation of the data.

Tacna Go ^® RT-PCR are commercially available equipment, for example, ABI Prism (PRISM) 7700TM Sequence Detection System ^{(Sequence Detection System) TM (Perkin} -Elmer-Applied Biosystems), or the light between the clutch (Lightcycler) (Roche Molecular Biochemicals).

The 5'-nuclease assay test data is initially expressed as Ct, or as a threshold cycle. The fluorescence value is recorded for each cycle and represents the amount of product amplified to that point by an amplification reaction. The point at which the fluorescence signal is initially recorded as statistically significant is the threshold cycle (Ct).

In order to minimize variation effects and errors between samples, RT-PCR is generally performed using reference RNA, which is ideally expressed at a constant level among different tissues, and is not affected by experimental treatment. The RNA most often used to standardize gene expression patterns is the mRNA of the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPD) and beta -actin (ACTB).

In addition, the new real-time quantitative PCR is a dual-labeled fluorescent immunogenic probe to the technology of measuring the PCR product accumulation through a (i.e., Tacna Go ^® probes), quantitative competitive PCR (in this case, the internal competitor for each target sequence is used to standardize ) And a standardized gene contained in a sample or a quantitative comparative PCR using a housekeeping gene for RT-PCR.

b. Massarray system

In a mass array-based gene expression profiling method developed by Sequenom, Inc., cDNAs obtained after isolation and reverse transcription of RNA are synthesized using a synthetic DNA molecule (competitor), which is targeted at all positions except for a single base lt; / RTI &gt; cDNA region), and used as an internal standard. The cDNA / competitor mixture is PCR amplified and subjected to post-PCR shrimp alkaline phosphatase (SAP) enzyme treatment resulting in the dephosphorylation of the remaining nucleotides. After inactivation of the alkaline phosphatase, the PCR product from the competitor and cDNA is primer-extended, which produces separate mass signals for the competitor- and cDNA-derived PCR products. After purification, these products are dispensed onto a chip array where the components necessary for analysis using matrix-assisted laser desorption ionization flow time mass spectrometry (MALDI-TOF MS) analysis are pre-loaded. The cDNA present in the reaction is then quantified by analyzing the peak area ratio in the resulting mass spectrum.

c. Other PCR-based methods

Other parallax displays; Amplified fragment length polymorphism (iAFLP); BeadArray TM technology (Illumina, San Diego, Calif.); A BeadsArray for Detection of Gene Expression using a commercially available Luminex 100 LabMAP system and multicolor-coded microspheres (Luminex Corp.) for rapid assay testing for gene expression. BADGE); And high-coat expression profile creation (HiCEP) analysis.

3. General description of mRNA isolation, purification and amplification

Techniques for creating gene expression profiles using tissue embedded in paraffin have been described above. The final data is analyzed to determine the best treatment option (s) available to the patient based on the characteristic gene expression pattern identified in the observed tumor sample.

An important aspect of the present invention is to provide prognostic information using measured expression of a particular gene by gastric cancer tissue. For this purpose, it is essential to calibrate (standardize) the amount of assayed RNA, variations in RNA quality used, and differences in other factors, such as machine and operator differences. Therefore, assays typically measure and incorporate the use of reference RNA, including those transcribed from known housekeeping genes such as GAPD and ACTB. Alternatively, standardization can be based on the mean or median signal (Ct) of all of the tested genes or a large number of subset thereof (full standardization approach). In the following examples, a centralized standardization strategy was used, which used a subset of screened genes selected based on lack of correlation with clinical outcomes for standardization.

The term "training set" means a subject sample from which a statistically significant RNA transcript is extracted for a prognosis.

The term "validation set" or "test set" refers to a set that tests the accuracy with which the extracted variables can actually be judged to be good or bad. The reason for using this method is that it is not only effective in determining the prognosis in a specific sample group but also is effective in an independent sample.

4. Prognostic indicators for recurrence and their application

Characteristics of the cancer prognostic method for the likelihood of recurrence of gastric cancer are characterized by 1) a set of unique test mRNAs (or corresponding gene expression products) used to measure the likelihood of recurrence, 2) the specific Weight, and 3) thresholds used to divide patients into groups of different levels of risk, such as low, middle, and high risk groups. This computation yields numerical risk scores (RS) and RS (%).

The tests require a laboratory test to determine the level of the indicated mRNA or their expression products, but may be either fresh or frozen tissue, or a fixed, paraffin-embedded tumor biopsy The test piece can be used in very small amounts. Thus, the test may be non-invasive. It is also compatible with several different methods of tumor tissue harvested, for example, through a core biopsy or fine needle aspiration. According to this method, the cancer risk score (RS)

(a) creating a gene or protein expression profile with a biological sample comprising cancer cells obtained from said subject;

(b) Quantifying the expression level of a plurality of individual genes (i.e., mRNA level) to determine the expression value for each gene;

(c) generating a subset of gene expression values, each comprising an expression value for genes linked by a cancer-related biological function and / or by co-expression;

(d) multiplying the expression of each gene in a subset by a coefficient that reflects a relative contribution to its cancer recurrence in said subset, and multiplying that value to yield a value for said subset;

(e) multiplying the value of each subset by a coefficient that reflects its contribution to cancer recurrence;

(f) obtaining the sum of the values for each subset multiplied by the coefficient to obtain a risk score (RS) and an RS (%), where the contribution of each subset not exhibiting a linear correlation with cancer recurrence Is included only at or above a predetermined threshold value,

Negative values are assigned to subsets in which the increased expression of a given gene reduces the risk of cancer recurrence and a positive value is given to a subset in which the expression of a specified gene increases the risk of cancer recurrence.

In a specific embodiment, RS and RS (%) are

(a) The expression levels of the RNA transcripts of CSK, CD8A and CHEK2 were measured,

(b) RS and RS (%) by the following equations 1 and 2:

[Equation 1]

RS = HR ₁ * normLogTransValue ₁ + HR ₂ * normLogTransValue ₂ + ... + HR _n * normLogTransValue _n

&Quot; (2) &quot;

RS (%) = 100 × (RS minimum value of the biological sample) / (RS maximum value of the population - RS minimum value of the population)

In this formula,

HR _n represents the hazard ratio of the n-th RNA transcript and is converted to -1 / HR _n when HR _n is less than 1,

normLogTransValue _n is a value associated with the expression of the RNA transcript and is a value obtained by varying the scale around the median based on the total value of the gene,

The population means a certain number of groups having a whole gastric cancer unrelated to the TNM stage, and the constant number is an arbitrary integer such that the RS maximum value and the minimum value can be calculated.

If the value of RS (%) is 75% or more, it is a bad prognosis. If it is less than 25%, it is judged that it is a good prognosis.

Hereinafter, the present invention will be described in detail by way of examples. However, the following examples are illustrative of the present invention, and the present invention is not limited to the following examples.

&Lt; Production example >

Tumor specimens and clinical data were obtained from gastric cancer patients (YUSH, n = 520) who underwent gastrectomy as primary treatment at Yonsei University Severance Hospital between 2001 and 2004. 1 to 4, but mainly in 2 and 3 (Fig. 1). All samples were collected after receiving detailed written consent from the patient, and the study was approved by the Research Ethics Committee at Severance Hospital, Yonsei University. Clinical data were obtained retrospectively. Overall survival was defined as the time from surgery to death, and the data were considered censored when the patient was alive at the last contact.

Table 1 lists the target genes (44) and the reference genes for correction (10) for the prognostic index using RNA transcripts in gastric cancer 2-3.

The level of mRNA species of the genes was measured by RT-PCR. mRNA is extracted using the master Pure TM RNA purification kit (Epicenter Technologies (Epicentre Technologies)), which was quantified by a revolving green (RiboGreen) ^® Fluorescence method (Molecular probes). Molecular assay of quantitative gene expression was performed using ROCHE 480. The ROCHE 480 consists of a thermocycler, a laser, a charge-coupled device (CCD), a camera and a computer. The system amplified the samples in a 384-well format on a thermocycler. During amplification, a laser-induced fluorescence signal was collected in real time for all 384-wells and detected on the CCD. The system includes software for running the instrument and for analyzing the data.

The threshold value cycle (CT) values for each patient were standardized based on an average of a subset of screened genes for a particular patient selected based on a lack of correlation with clinical outcome (centralized standardization strategy).

Since it is common practice to use reference genes as a means of calibrating gene expression, the "Normfiner" program was used to standardize six of the 10 reference genes. ATP5E, GPX1, PGK1, UBB, VDAC2, and ACTB used for standardization (Fig. 2).

The primers used in qRT-PCR were those listed in Tables 1 and 2 of U.S. Patent No. 7526387.

To construct a prognostic prediction model, cluster analysis was performed according to Cluster and Treeview ( http://rana.lbl.gov/EigenSoftware.htm ). Autonomous clustering analysis was performed after continuous dispersion filtration and the prognostic differences of the two taxa of two major clusters were tested according to the log rank test and the Kaplan Meier plot.

<Example 1> Gene expression profile of gastric cancer patients

FIG. 3 shows a heat map of the results of qPCR of 520 samples, showing low expression in red color and high expression in green line, and 520 samples showed good prognosis (CL2) and bad prognosis (CL1 ). Kaplan Meier Plot and log rank test showed a p-value of 0.0233.

Example 2: Survival risk prediction using BRB-Array Tools (PCA analysis)

Table 2 below shows the genes used in the classification of the risk groups and is analyzed by PCA (Principal Component Analysis) using BRB ArrayTools Version 4.1 ( http://linus.nic.nih.gov./BRB-ArrayTools.html ) The Prognostic Index was calculated. That is, the wi value is multiplied by the intermediate expression value of the gene and the value calculated by the following formula is divided into two layers.

Σ _i w _i x _i - 0.007356

FIG. 4 shows the survival risk prediction result according to the PCA analysis using the BRB-Array Tools.

In order to measure the accuracy of this model according to the above formula, a compound covariate predictor (CCP), a diagonal linear discriminant analysis (LDA), a 1-nearest neighbor (NN), a 3-nearest neighbors Centroid), SVM (Support Vector Machines), and BCCP (Bayesian Compound Covariate Predictor).

p-value % CV Support Weights (wi) designation symbol One 0.00173 100 0.119466 CSK CSK 2 0.0012782 100 0.112631 CD8A CD8A 3 0.014359 90 0.106045 CHEK2 CHEK2

Using the above prognostic index, the results of dividing into two groups of the upper 50% and lower 50% are shown as CCP, LDA, 1-NN, 3-NN, NC, SMV and BCCP for each prediction model as shown in FIG. 5 .

CCP LDA 1-NN 3-NN NC SVM BCCP 76 80 83 82 78 92 89

Example 3 Prediction of Survival Risk Using BRB-Array Tools (HR Analysis)

As shown in the heat map of FIG. 3, three genes were selected by performing a Cox's proportional harzard test on gene expression. The tool used was BRB ArrayTools Version 4.1 ( http://linus.nic.nih.gov./BRB-ArrayTools.html ), which was used to set the algorithm related to the hazard ratio.

The prognostic risk rating system by qPCR converts the standardized gene expression level to RG (Relative Quantity) and calculates the hazard ratio (HR) by Cox regression analysis of the gene expression amount (Cq) value changed by RQ . The risk coefficient was converted to -1 / HR and multiplied by the amount of gene expression to calculate the sum (RS, Risk Score) (cut-off: -23 or less is considered to be -23). The percentile risk score was calculated according to the following formula:

RS (%) = 100 × (RS minimum value of the sample) / (RS maximum value of the population - RS minimum value of the population)

Samples with greater than 75% RS (%) were classified as high risk and samples with RS (%) ranging from 25% to less than 75% were classified as intermediate risk. Finally, samples with less than 25% RS (%) were classified as low risk.

Table 4 above shows the genes used in the classification of risk groups and their hazard coefficients.

Parametric p-value Hazard Ratio UniqueID 0.0011978 0.759 CSK 0.0013091 0.847 CD8A 0.0153688 0.79 CHEK2

As shown in Figure 6, a total of 520 samples of low-risk patients (24% of total deaths) accounted for 20% of the total deaths (22.2% of deaths) 33.5%) and 48 of the 113 patients in the high-risk group died (total 21.6%, mortality rate 42.5%).

As a result, stomach cancer has shown the possibility of staging using molecular features. From the results of qPCR for FFPE samples of stomach cancer, we confirmed that prognosis of clinical outcome is possible after gastrectomy through RS (%) obtained from selected gene list.

Claims (11)

In a biological sample containing cancer cells from a subject
Determining the degree of expression of the RNA transcript of CSK, CD8A and CHEK2; And
(RS, Risk Score) and the RS percentage (RS (%)) of the biological sample are calculated according to the following equations (1) and (2) based on the expression level of the RNA transcript determined in the step
And determining the prognosis in terms of overall survival (OS) without recurrence of gastric cancer after surgical resection of the whole gastric cancer regardless of the TNM stage according to the RS (%). Method of providing:
[Equation 1]
RS = HR ₁ * normLogTransValue ₁ + HR ₂ * normLogTransValue ₂ + ... + HR _n * normLogTransValue _n
&Quot; (2) &quot;
RS (%) = 100 × (RS minimum value of the biological sample) / (RS maximum value of the population - RS minimum value of the population)
In this formula,
HR _n represents the hazard ratio of the n-th RNA transcript and is converted to -1 / HR _n when HR _n is less than 1,
normLogTransValue _n is a value associated with the expression of the RNA transcript and is a value obtained by varying the scale around the median based on the total value of the gene,
The population means a certain number of groups having a whole gastric cancer unrelated to the TNM stage, and the constant number is an arbitrary integer such that the RS maximum value and the minimum value can be calculated.
delete delete The method according to claim 1,
This method is considered to be a high-risk group when the RS (%) of the biological sample is more than 75% in terms of the overall survival rate (OS), an intermediate risk group in the range of 25% to less than 75%, and a low- How it is.
In a biological sample containing cancer cells from a subject
Measuring the expression levels of RNA transcripts of CSK, CD8A and CHEK2; And
The increase in the expression of the transcript is judged as an increase in the possibility of a positive clinical result in terms of overall survival (OS) without recurrence of gastric cancer after surgical resection of the entire gastric cancer regardless of the TNM stage. A method of providing information for predicting prognosis.
6. The method of claim 5,
Wherein the method is a qPCR-based method.
delete delete A computer-readable recording medium recording a program for predicting the prognosis of stomach cancer, comprising: a nucleic acid sample
Determining the degree of expression of the RNA transcript of CSK, CD8A and CHEK2; And
(RS, Risk Score) and the RS percentage (RS (%)) of the sample are calculated according to the following equations (1) and (2) based on the degree of expression of RNA determined in the above step,
Patients with high-risk patients with> 75% of the RS (%) setting range in terms of overall survival (OS) without recurrence of gastric cancer after surgical resection of the entire gastric cancer, regardless of TNM stage, %, &Lt; / RTI &gt; a low risk group if less than &lt; RTI ID = 0.0 &gt; 25% &lt; / RTI &gt;
[Equation 1]
RS = HR ₁ * normLogTransValue ₁ + HR ₂ * normLogTransValue ₂ + ... + HR _n * normLogTransValue _n
&Quot; (2) &quot;
RS (%) = 100 × (RS minimum value of the RS in the sample) / (RS maximum value in the population - RS minimum value in the population)
In this formula,
HR _n represents the hazard ratio of the n-th RNA transcript and is converted to -1 / HR _n when HR _n is less than 1,
normLogTransValue _n is a value associated with the expression of the RNA transcript and is a value obtained by varying the scale around the median based on the total value of the gene,
The population means a certain number of groups having a whole gastric cancer unrelated to the TNM stage, and the constant number is an arbitrary integer such that the RS maximum value and the minimum value can be calculated.
delete delete

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