WO2006135220A1 - Method for selecting gastric cancer specific predictor genes and use thereof - Google Patents

Method for selecting gastric cancer specific predictor genes and use thereof Download PDF

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WO2006135220A1
WO2006135220A1 PCT/KR2006/002340 KR2006002340W WO2006135220A1 WO 2006135220 A1 WO2006135220 A1 WO 2006135220A1 KR 2006002340 W KR2006002340 W KR 2006002340W WO 2006135220 A1 WO2006135220 A1 WO 2006135220A1
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genes
gastric cancer
genbank
tissue
gastric
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PCT/KR2006/002340
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French (fr)
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Sang-Hwa Yang
Hei-Cheul Jeung
Sun-Young Rha
Sung-Whan An
Jae-Kyung Roh
Se-Wang Yoon
Hyun-Cheol Chung
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Industry-Academic Coopration Foundation, Yonsei University
Ic-Gen Inc.
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Publication of WO2006135220A1 publication Critical patent/WO2006135220A1/en

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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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    • C12Q1/6813—Hybridisation assays
    • C12Q1/6834—Enzymatic or biochemical coupling of nucleic acids to a solid phase
    • C12Q1/6837—Enzymatic or biochemical coupling of nucleic acids to a solid phase using probe arrays or probe chips
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

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  • gastric cancer is treated with one of three therapies, i.e., surgical operation, irradiation and chemotherapy, or a combination thereof, and particularly the treatment of gastric cancer consists mostly of surgical excision and chemotherapy that uses drugs.
  • gastric cancers have been classified according to the clinical and morphological characteristics thereof, patients, who could be classified differently in terms of the genetic level, would receive the same diagnosis.
  • a gastric cancer therapeutic method and therapeutic drug selected according to prior classification were applied, there were many cases in which gastric cancer was not cured. Accordingly, there has been a need to develop drugs which can be efficiently applied to all kinds of gastric cancers that can occur in one group according to each classification.
  • the present invention provides a cDNA microarray for the diagnosis of gastric cancer, which has the following genes fixed on a substrate: GenBank ID NOs: R72097, R71093, AA521228, R51912, AI733427, AW058221, H38240, AIOOl 183, AI002047, and AI913412, wherein said genes have a gastric cancer prediction strength of 37.94.
  • the cDNA microarray may comprises at least one gene which is additionally fixed on a substrate, said gene selected from the group consisting of GenBank ID NOs: AI766870; AI672312; AA457710; AA928660; N63988; AI990922; AA444051; AW072982; AA482119; R56211; AA669637; AA450009; AI254931; AA279025; AA455157; AA490929; AA973568; AI469062; AI299356; AA971493; N92901; AA002126; AA416890; AI961583; H39192; AA419460; AA478298; AI889705; H17034; N66177; AA035384;
  • AI299348 AI302661; AI056417; AA911832; AA676471; AA427954; AA453015; AA913197; AA700876; AI146764; AA916857; AI439171; AA491209; AI739498;
  • FIG. 1 is a general schematic diagram of the present invention and shows an analysis method comprising microarray experiment, gene selection, cross- validation and prediction.
  • GeneSpring 7.0 software (Silicon Genetics, Redwood City, CA) was used to perform normalization of raw data, filtering of genes, classifier gene selection, cross-validation of a training set, and class prediction of a test set and independent set.
  • the prediction strengths were calculated as the negative natural logarithm of p- values. Based on the prediction strengths, said 894 genes were grouped. In this system, the probability of acquisition of "ideal expression pattern" of each gene in a sample observed from each class (normal or tumor) can be calculated (Golub, T.R., Science, 286:531, 1999).
  • a forward primer of SEQ ID NO: 5 (5'- ACGAGGACAT AGATGACGAC-3 ') and a reverse primer of SEQ ID NO: 6 (5- TTTACAAATATCACCCCGTC-3') were used.
  • a forward primer of SEQ ID NO: 7 (5'-AGCTCACCTATTTGCATCAT- 3 ') and a reverse primer of SEQ ID NO: 8 (5 '-CTCTGTAATTGCGACATGAA- 3') were used.
  • gastric cancer-specific genes can be selected in a rapid and cost-effective manner compared to the prior methods, including visual gastric cancer tissue examination method and biochemical gastric cancer detection method requiring a long period of time. Also, the use of the microarray having the selected genes fixed thereon allows gastric cancer to be diagnosed in a simple and accurate manner. In addition, when genes, which are overexpressed specifically in gastric cancer tissue, are used, it is possible to screen anticancer drugs to kill gastric cancer cells.

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Abstract

The present invention relates to a method for selecting gastric cancer-predictor genes and the use thereof, and more particularly to a method for selecting gastric cancer-specific genes capable of accurately predicting gastric cancer, the method comprising: analyzing gene expression profiles of normal gastric tissue and gastric cancer tissue using a cDNA microarray; selecting genes which are expressed differentially in the gastric cancer tissue using one-way ANOVA; and grouping the selected genes according to the prediction strength thereof, as well as a probe composition and microarray for diagnosing gastric cancer, comprising the selected genes. According to the present invention, gastric cancer-specific genes can be selected in a rapid and cost-effective manner using a cDNA microarray analysis method, and gastric cancer can be diagnosed in a simple and accurate manner using the microarray having the selected genes fixed thereon.

Description

Method for Selecting Gastric Cancer Specific Predictor Genes and Use Thereof
TECHNICAL FIELD
The present invention relates to a method for selecting gastric cancer predictor genes and the use thereof, and more particularly to a method for selecting gastric cancer-specific genes capable of precisely predicting gastric cancer, the method comprising: analyzing gene expression profiles of normal gastric tissue and gastric cancer tissue using a cDNA microarray; selecting genes which are expressed differentially in the gastric cancer tissue using one-way ANOVA; and classifying the selected genes according to the prediction strength thereof, as well as a probe composition and microarray for diagnosing gastric cancer, comprising the selected genes.
BACKGROUND ART
Gastric cancer is one of the most prevalent cancers worldwide. Gastric cancer is late-onset disease, and the incidence of gastric cancer in Asian countries such as Korea and Japan is the first or second highest among cancers. Unlike other cancers, gastric cancer has high resistance to chemotherapy and is a late-onset disease, and thus the early diagnosis of gastric cancer is most effective for the prevention and treatment thereof. Currently, the diagnosis of gastric cancer merely depends on endoscopy, which is expensive and cannot distinguish various different kinds of gastric cancers from each other. Thus, even if gastric cancer is detected by endoscopy, there is much difficulty in selecting a suitable therapeutic method and therapeutic drug according to the kinds of gastric cancer.
Currently, cancer is treated with one of three therapies, i.e., surgical operation, irradiation and chemotherapy, or a combination thereof, and particularly the treatment of gastric cancer consists mostly of surgical excision and chemotherapy that uses drugs. Particularly, it is very important for the complete cure of gastric cancer to use a method for the systemic detection of gastric cancer tissue and precisely classify detected gastric cancer tissue upon the initial clinical diagnosis of gastric cancer. In other words, because gastric cancers have been classified according to the clinical and morphological characteristics thereof, patients, who could be classified differently in terms of the genetic level, would receive the same diagnosis. For this reason, when a gastric cancer therapeutic method and therapeutic drug selected according to prior classification were applied, there were many cases in which gastric cancer was not cured. Accordingly, there has been a need to develop drugs which can be efficiently applied to all kinds of gastric cancers that can occur in one group according to each classification.
For this purpose, it is particularly important to select a target gene for the treatment of gastric cancer. In other words, the differentiation degree and invasion properties of gastric cancer and the expression pattern and level of genes vary according to the stage of gastric cancer. Thus, in order to develop drugs which can be suitably administered according to the classification of gastric cancer, it is important to select a target gene suitable for each stage and to find a substance that can regulate the function of the target gene.
Thus, many pharmaceutical companies and research institutes have conducted many studies to find anticancer drug candidates which can be used suitably according to the classification of gastric cancer, but these studies have been carried out by random approaches, which mean dramatic increases in research expenses and time. Even if anticancer drug candidates were found, these drugs often show side effects in clinical tests, and thus it was very difficult to develop anticancer drugs having no side effect. Furthermore, if the mechanism of action of the found anticancer drug candidates in gastric cancer treatment was not clearly established, it was difficult to apply them according to the classification of gastric cancer, which also mean dramatic increases in research expenses and time in establishing the mechanism of action of these drugs.
Accordingly, in the technical field to which the present invention pertains, there has been an urgent need for a method for objectively diagnosing gastric cancer in the genetic level and a novel method capable of substituting for the anticancer drug development method that uses the random approaches.
Accordingly, the present inventors have made extensive efforts to select genes which are overexpressed or lowly expressed specifically in gastric cancer tissue and, as a result, identified gastric cancer-specific genes which are obtained by microarray analysis, thereby completing the present invention.
SUMMARY OF INVENTION
In one aspect, the present invention relates to a method for selecting gastric cancer- specific genes capable of precisely predicting gastric cancer, the method comprising: analyzing the gene expression profiles of normal gastric tissue and gastric cancer tissue using a cDNA microarray; selecting genes showing a change in expression in gastric cancer tissue compared to normal gastric tissue using oneway ANOVA; and classifying the selected genes according to the prediction strength thereof.
In another aspect, the present invention relates to a probe composition for the diagnosis of gastric cancer, the composition comprising oligonucleotides derived from a sequence selected from the group consisting of genes selected using said method. In still another aspect, the present invention relates to a microarray for the diagnosis of gastric cancer, the microarray comprising the above-selected genes or their fragments fixed thereon.
The above and other objects, features and embodiments of the present invention will be more clearly understood from the following detailed description and accompanying claims.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 is a schematic diagram generally showing a microarray experiment and analysis process according to the present invention.
FIG. 2 is a graphic diagram showing that 894 genes are grouped according to the prediction strength thereof. In FIG. 2, the X-axis indicates numbers of genes ranging from 10 to 894, and the Y-axis indicates prediction strength.
FIG. 3 is a dendrogram showing the unsupervised hierarchical clustering of 12,891 genes and 29 pairs of gastric tissue samples in a training set. In (A), the horizontal axis indicates the genes, and the longitudinal axis indicates the samples. Also, (B) indicates the two-way hierarchical clustering of 92 genes and 58 samples in a training set.
FIG. 4 is a photograph showing the results of H&E staining conducted to validate the accuracy of prediction based on the gene expression of gastric tissue.
FIG. 5 is a graphic diagram showing the correlation between microarray and RT- PCR data. (A) is a bar graph showing the average value of the expression levels of five genes, which are measured using microarray and RT-PCR in the level of each of the genes. (B) is a graph showing the correlation between microarray and real-time RT-PCR data.
DETAILED DESCRIPTION OF THE INVENTION,
AND PREFERRED EMBODIMENTS
In one aspect, the present invention provides a method for selecting gastric cancer- specific genes, the method comprising the steps of: (a) analyzing gene expression profiles of normal gastric tissue and gastric cancer tissue using a cDNA microarray; (b) dividing the gene expression profile analysis results into a training set, a test set and an independent set; (c) selecting genes differentially expressed in the gastric cancer tissue and the normal tissue from among the genes in the training set using one-way ANOVA; (d) classifying the selected genes according to the prediction strength thereof; and (e) validating the prediction accuracy of the selected genes by cross-validation based on a k-nearest neighbor method and real-time PCR analysis.
In another aspect, the present invention provides a probe composition for diagnosing gastric cancer, the composition containing 10-200 consecutive oligonucleotides of GenBank ID NO: R72097, 10-200 consecutive oligonucleotides of GenBank ID NO: R71093, 10-200 consecutive oligonucleotides of GenBank ID NO: AA521228, 10-200 consecutive oligonucleotides of GenBank ID NO: R51912, 10-200 oligonucleotide of GenBank ID NO: AI733427, 10-200 consecutive nucleotides of GenBank ID NO: AW058221 , 10-200 consecutive oligonucleotides of GenBank ID NO: H38240, 10- 200 consecutive oligonucleotides of GenBank ID NO: AIOOl 183, 10-200 consecutive oligonucleotides of GenBank ID NO: AI002047, and 10-200 consecutive oligonucleotides of GenBank ID NO: AI913412, wherein said consecutive oligonucleotides have a gastric cancer prediction strength of 37.94. In the present invention, the probe composition may additionally contains 10-200 consecutive oligonucleotides derived from at least one sequence selected from the group consisting of GenBank ID NOs: AI766870, AI672312, AA457710;
AA928660; N63988; AI990922; AA444051; AW072982; AA482119; R56211; AA669637; AA450009; AI254931; AA279025; AA455157; AA490929;
AA973568; AI469062; AI299356; AA971493; N92901; AA002126; AA416890;
AI961583; H39192; AA419460; AA478298; AI889705; H17034; N66177;
AA035384; AA454732; AA987627; AA845156; AI364113; AA983158;
AI225185; AA419488; AA459305; AA922939; AA404486; AI365395; AI291174; AA459109; AA454652; AA888224; AA152496; AA973703; AI383789; T71879;
AI936324; N62620; AA935508; AA447781; AA287695; AI356106; AA017383;
N35888; AI871588; AI365075; AI635529; AA701652; AA991346; AI984082;
AA935273; AI360385; AA725397; AA436152; AA292226; AA025276;
AA446259; AI254559; AA488075; AI438930; AA418674; AI302139; AA488084; AI299348; AI302661; AI056417; AA911832; AA676471; AA427954; AA453015;
AA913197; AA700876; AI146764; AA916857; AI439171; AA491209; AI739498;
AA436479; AI473896; AA496792; AA775616; AA457114; W73874; AA172400;
H60549; R55786; AI925568; AA598865; AI922341; AI493478; AA046430;
AA418104; AI971229; AI382541; AA629591; AA863149; AA489246; AA857101; AI361897; N33920; AA702193; AI081973; R02085; R47893;
AA457084; AA281929; AA863125; AA088420; AI652076; R05416; AI160757;
N39161; AI310461; AA214530; AA913079; AA489587; AI654147; AI654494;
AA669689; AI272002; AA425022; AA700060; AA873499; AI884731; AI337099;
AI337428; AA292410; AI346147; AA426025; AI521155; AA432084; AA911705; AA427561; AA598478; AW082097; R56149; H17883; AI299378; AA777410;
N81029; AA279396; AI475805; AI950312; AA598610; T96082; T61948;
AA149987; AA497002; AI985398; AI318311; AA458878; AA461118; AI862416;
AI361715; AA425450; AI675311; W73790; AI769340; AA452278; AA598965;
AI655392; AA400464; AA988586; AA047567; AW008721; AI681849; AA455111; AI300810; AA432063; AI492016; AI291184; AA699469; H25917; AI423445; AA490011; AW072762; AI263104; AA504465; AA278534;
AW003596; AI076929; AA489666; AA458634; AI299221; AA887320;
AA454588; AA989217; AI668916; R93124; AI216056; T41173; AA775257;
AW025920; AA989521; AI796806; W76339; AA961116; AA971398; N76927; AI416975; W96155; AI310550; AA402754; AA975556; N30553; AA489100;
AI565424; AA488341; AA211855; AI357590; AA460282; AA280846; AI797648;
AI341318; AI674349; AA488618; AA454540; N29639; AA863225; AA936133;
AA680186; AI401608; AI292232; T71976; AA459265; AI299073; AA928017;
AA442092; AI254648; AW073291; AA125872; AA404387; AW073055; AI131555; W49563; R60301; AA496022; AA485303; AI268697; AA460833;
N95761; AA461157; AA479344; AI624388; N80235; H26184; AI652005;
AI380209; AA916327; AI811492; AI016689; AI438958; AI679562; AI267935;
AA857035; R43873; AI261783; AA486261; T70057; AI675714; AI631139;
AA490887; AA933888; N64679; AA412433; H24326; AI146507; R75635; AI814648; AI814383; AAOl 1414; AA100036; AI301699; AI652207; AI261737;
AI400273; H17615; AI016025; AA505045; AI871665; R40897; AA055486;
AI524284; AI350153; AA464590; AI207000; AA830392; AA521373; AA911045;
AA939219; AI927438; H22944; AA779401; AA150828; AA452353; AI361561;
AA927663; AI658727; AI299994; AI676097; AA430540; AA419251; AI340188; R45009; N72137; AI363203; AI097617; AA156988; T55560; AA196287;
AI016051; AI361422; T71991; AA279337; H15155; AA885869; AI301123;
AA479745; AA482286; H65030; AA457051; AA453774; AI318421; AAl 69469;
H96643; AA953560; AW058344; AA505051; T72336; AA863093; H84982;
AI701664; AA207165; W72294; AA504253; R56774; AI363436; AI924634; AA677534; AI684973; AI253074; AI383503; R85257; AI768615; AI268273;
AA488070; AA775212; AI341604; AI971009; AA932813; W72792; AW057803;
AA668470; AA454597; AA485865; AA885835; AI433513; AA464217;
AI341160; AA458965; AA459401; AI421677; AA664101; AA705720;
AA279804; AI262070; AA102526; AA976851; R43532; AI261207; H73234; AI311391; AA922859; R76099; AA887001; AI340883; AI523637; H62387; AA916325; AA865464; AA989210; AA488996; AI979290; AA917683;
AA857343; AA913127; AI538184; AA873089; AA888213; AA431832;
AA934764; AI369830; N70463; AI302205; AA479199; AI301175; AA424833;
H14372; AA935570; AI301681; AA497033; AI369713; AI766746; AA430443; AW009108; AI056539; AA928708; AA598776; AA504348; AA070226;
AA235332; T65407; AA458982; AA845178; N91385; AA479795; AI418200;
AA989497; H57180; T67006; AI611956; AA931930; AI473336; AI739206;
AI336946; AA488406; AI989344; AW050484; AA845167; AA054073;
AA018980; AI016021; AA412053; AI244667; AI393019; AI024088; AI650283; AI356451; AI858088; AA878040; N59532; AI017670; AA775738; AI758888;
AA155640; AI802786; AA976691; AI261660; AA406020; AI282021; AA932558;
N74637; AA988630; R32848; AA037014; AI301815; AI493046; AI986317;
N33214; AA625888; AI017801; AI963860; AA489017; AI309187; AA504259;
AI968672; W46577; N53380; AA864861; AA922800; AA446477; AA932295; AA923696; W06980; AA857944; AA188179; AA676223; AW058504;
AA873159; AA453816; AW075163; AA994760; AA894855; AA459364;
AA701655; AA191245; AI369378; AA159620; H18932; AI289178; AA136983;
AW072500; AI418753; AI634715; AA131406; T98612; AI611214; AA459012;
AA975820; AW081868; H52119; R89492; H57494; AI342012; AA932564; AA490606; N63845; AI298493; AA976544; AI701018; R92425; H45668;
AA664180; AW029441; AA939100; N71003; AI057267; AA489045; AI271909;
AI830324; N31492; AI418194; AI271987; AI890849; AA954935; AI538192;
AA001449; H77652; AA991889; AA995197; AW075162; AA490466; AA487488;
R31701; AI261377; AA043343; AA279980; AI245812; H23187; AI346878; AI095381; N62179; AA045320; AA629687; AA932135; H19440; AA916780;
H53340; AA932983; AA496283; Hl 5574; AI689831; AA496360; T57841 ;
H65660; AA683077; AI348319; AA427940; AA442853; AA977679; W46900;
AA676663; AI418741; AA485151; T73468; AA101875; H94487; AI337108;
AI924973; AA490497; H69561; AI301329; R45026; AI569017; AI870821; T63511; AI925826; AI289185; H79047; AA677432; AA282063; AI989728; AW058317; AA464417; AA487526; AA280692; AA464856; AA987705;
AI675465; AA486324; AI244615; AI214500; AA143331; W72207; AI366840;
AA987658; AA159577; T73558; AI312971; AA634308; AI859300; AI813911;
AI310113; H78386; AA977242; AI521932; AI261741; AI147534; AI924357; AA477283; AA862966; AA521439; AA857542; AA775223; T86934; AA976699;
AA486082; T60163; AA922832; AA485371; R67275; AA400258; AA018683;
AI379365; AI536541; AA055163; T65736; AA677706; AA970531; AI418638;
R91078; AA460291; AA504492; H50500; AA423867; AI459325; AI828306;
AI815143; T71349; H95960; AA844831; AW057804; N24824; AA434115; AA496997; AA625655; AW073502; AI367796; AA461456; AA281635;
AI000103; AI364688; AIO 14441; AI253136; AI337294; AW001034; AA430698;
T72076; AA704407; N64384; AA464152; W88655; AA142922; AI888275;
AI337340; AA934734; AA441933; AA872383; AA862465; AI628353; N80129;
AA069024; AI640779; AI241337; AA406601; AA974008; R17765; AA504141; AI311655; AA598611; AI160214; AA449742; AI016010; R40400; H72028;
AA455925; AI982577; AI289110; AI791122; AA086476; AA287550; AA423957;
AI863845; AI261360; AI422367; AW009769; AI986458; AA630800; AI655374;
AA894557; AI924452; AI291863; N70714; N78902; AI333599; AI350508;
AA961735; H73590; AA448478; T53298; AA025150; AI933187; AA923567; AI241301; AA455369; AI524093; AA490903; AI015711; AI824922; H29295;
AI057229; AI261290; AA102454; AA485893; N30096; AA921679; AI432357;
AA857098; A971274; AA989500; AA034939; AI362919; AI148329; T50675;
AA464196; AA863314; AI991902; R99562; AA465233; AI422138; AA133469;
AA682897; AI375353; AW057705; AI150389; AI381043; AA985421; AA436401; W84701; AA458779; AI823975; AI005513; AA669452; N30372;
AW028846; AA970402; AA431347; AA283023; AI366996; AI032392;
AA975430; AA425217; AA974221; W92764; R62603; AI017394; AI005521;
AA280832; AI017442; AI041729; AW006385; AA775957; AI309109;
AA968896; R48303; AI290905; AA947730; AI074272; AI936084; AI023541; AI356027; AI659370; T94781; AI799888; AA126989; AI356028; AA975832; AA931898; AA456852; AA405569; H72723; AA447079; AI951084; AA894763; AI394646; AW008766; T62865; AA150402; AI423270; AI620493; AA490172; AI537061; AA453258; AI657057; N71159; AA521345; AI674972; AI884374; AA446017; AA455261; AI000966; AA931118; AI924306; AA663309; AI470775; AA991590; AA487193; AI350347; AI652557; AA864299; AA974305; R91950; AA865554; AA999838; AW071052; AA434102; AA521358; W32272; AA911063; AI473897; AA775091; AA857015; AA862436; AI924753; AA430665; T66832; AI301513; AA971278; AI745626; AAOl 1096; AA152347; AA873577; AI269774; AA931491; AI923970; AA857437; AA664009; R91396; AI887514; AW028368; AA029283; AI368479; N68159; AA862371; AI375428; AI473884; T70999; AI368486; AI000474; AA676836; N67017; N53136; AI362740; AI560668; AA029299; AA932696; AI279830; AA476461;AI868227 and AI081269.
In still another aspect, the present invention provides an oligonucleotide microarray for the diagnosis of gastric cancer, which has said probe composition fixed on a substrate.
In yet another aspect, the present invention provides a cDNA microarray for the diagnosis of gastric cancer, which has the following genes fixed on a substrate: GenBank ID NOs: R72097, R71093, AA521228, R51912, AI733427, AW058221, H38240, AIOOl 183, AI002047, and AI913412, wherein said genes have a gastric cancer prediction strength of 37.94.
In the present invention, the cDNA microarray may comprises at least one gene which is additionally fixed on a substrate, said gene selected from the group consisting of GenBank ID NOs: AI766870; AI672312; AA457710; AA928660; N63988; AI990922; AA444051; AW072982; AA482119; R56211; AA669637; AA450009; AI254931; AA279025; AA455157; AA490929; AA973568; AI469062; AI299356; AA971493; N92901; AA002126; AA416890; AI961583; H39192; AA419460; AA478298; AI889705; H17034; N66177; AA035384;
AA454732; AA987627; AA845156; AI364113; AA983158; AI225185;
AA419488; AA459305; AA922939; AA404486; AI365395; AI291174;
AA459109; AA454652; AA888224; AA152496; AA973703; AI383789; T71879; AI936324; N62620; AA935508; AA447781; AA287695; AI356106; AA017383;
N35888; AI871588; AI365075; AI635529; AA701652; AA991346; AI984082;
AA935273; AI360385; AA725397; AA436152; AA292226; AA025276;
AA446259; AI254559; AA488075; AI438930; AA418674; AI302139; AA488084;
AI299348; AI302661; AI056417; AA911832; AA676471; AA427954; AA453015; AA913197; AA700876; AI146764; AA916857; AI439171; AA491209; AI739498;
AA436479; AI473896; AA496792; AA775616; AA457114; W73874; AA172400;
H60549; R55786; AI925568; AA598865; AI922341; AI493478; AA046430;
AA418104; AI971229; AI382541; AA629591; AA863149; AA489246;
AA857101; AI361897; N33920; AA702193; AI081973; R02085; R47893; AA457084; AA281929; AA863125; AA088420; AI652076; R05416; AI160757;
N39161; AI310461; AA214530; AA913079; AA489587; AI654147; AI654494;
AA669689; AI272002; AA425022; AA700060; AA873499; AI884731; AI337099;
AI337428; AA292410; AI346147; AA426025; AI521155; AA432084; AA911705;
AA427561; AA598478; AW082097; R56149; H17883; AI299378; AA777410; N81029; AA279396; AI475805; AI950312; AA598610; T96082; T61948;
AA149987; AA497002; AI985398; AI318311; AA458878; AA461118; AI862416;
AI361715; AA425450; AI675311; W73790; AI769340; AA452278; AA598965;
AI655392; AA400464; AA988586; AA047567; AW008721; AI681849;
AA455111; AI300810; AA432063; AI492016; AI291184; AA699469; H25917; AI423445; AA490011; AW072762; AI263104; AA504465; AA278534;
AW003596; AI076929; AA489666; AA458634; AI299221; AA887320;
AA454588; AA989217; AI668916; R93124; AI216056; T41173; AA775257;
AW025920; AA989521; AI796806; W76339; AA961116; AA971398; N76927;
AI416975; W96155; AI310550; AA402754; AA975556; N30553; AA489100; AI565424; AA488341; AA211855; AI357590; AA460282; AA280846; AI797648; AI341318; AI674349; AA488618; AA454540; N29639; AA863225; AA936133;
AA680186; AI401608; AI292232; T71976; AA459265; AI299073; AA928017;
AA442092; AI254648; AW073291; AA125872; AA404387; AW073055;
AI131555; W49563; R60301; AA496022; AA485303; AI268697; AA460833; N95761; AA461157; AA479344; AI624388; N80235; H26184; AI652005;
AI380209; AA916327; AI811492; AI016689; AI438958; AI679562; AI267935;
AA857035; R43873; AI261783; AA486261; T70057; AI675714; AI631139;
AA490887; AA933888; N64679; AA412433; H24326; AI146507; R75635;
AI814648; AI814383; AAOl 1414; AA100036; AI301699; AI652207; AI261737; AI400273; H17615; AI016025; AA505045; AI871665; R40897; AA055486;
AI524284; AI350153; AA464590; AI207000; AA830392; AA521373; AA911045;
AA939219; AI927438; H22944; AA779401; AA150828; AA452353; AI361561;
AA927663; AI658727; AI299994; AI676097; AA430540; AA419251; AI340188;
R45009; N72137; AI363203; AI097617; AA156988; T55560; AA196287; AIOl 6051; AI361422; T71991; AA279337; H15155; AA885869; AI301123;
AA479745; AA482286; H65030; AA457051; AA453774; AI318421; AAl 69469;
H96643; AA953560; AW058344; AA505051; T72336; AA863093; H84982;
AI701664; AA207165; W72294; AA504253; R56774; AI363436; AI924634;
AA677534; AI684973; AI253074; AI383503; R85257; AI768615; AI268273; AA488070; AA775212; AI341604; AI971009; AA932813; W72792; AW057803;
AA668470; AA454597; AA485865; AA885835; AI433513; AA464217;
AI341160; AA458965; AA459401; AI421677; AA664101; AA705720;
AA279804; AI262070; AA102526; AA976851; R43532; AI261207; H73234;
AI311391; AA922859; R76099; AA887001; AI340883; AI523637; H62387; AA916325; AA865464; AA989210; AA488996; AI979290; AA917683;
AA857343; AA913127; AI538184; AA873089; AA888213; AA431832;
AA934764; AI369830; N70463; AI302205; AA479199; AI301175; AA424833;
H14372; AA935570; AI301681; AA497033; AI369713; AI766746; AA430443;
AW009108; AI056539; AA928708; AA598776; AA504348; AA070226; AA235332; T65407; AA458982; AA845178; N91385; AA479795; AI418200; AA989497; H57180; T67006; AI611956; AA931930; AI473336; AI739206;
AI336946; AA488406; AI989344; AW050484; AA845167; AA054073;
AA018980; AI016021; AA412053; AI244667; AI393019; AI024088; AI650283;
AI356451; AI858088; AA878040; N59532; AI017670; AA775738; AI758888; AA155640; AI802786; AA976691; AI261660; AA406020; AI282021; AA932558;
N74637; AA988630; R32848; AA037014; AI301815; AI493046; AI986317;
N33214; AA625888; AI017801; AI963860; AA489017; AI309187; AA504259;
AI968672; W46577; N53380; AA864861; AA922800; AA446477; AA932295;
AA923696; W06980; AA857944; AAl 88179; AA676223; AW058504; AA873159; AA453816; AW075163; AA994760; AA894855; AA459364;
AA701655; AA191245; AI369378; AA159620; H18932; AI289178; AA136983;
AW072500; AI418753; AI634715; AA131406; T98612; AI611214; AA459012;
AA975820; AW081868; H52119; R89492; H57494; AI342012; AA932564;
AA490606; N63845; AI298493; AA976544; AI701018; R92425; H45668; AA664180; AW029441; AA939100; N71003; AI057267; AA489045; AI271909;
AI830324; N31492; AI418194; AI271987; AI890849; AA954935; AI538192;
AA001449; H77652; AA991889; AA995197; AW075162; AA490466; AA487488;
R31701; AI261377; AA043343; AA279980; AI245812; H23187; AI346878;
AI095381; N62179; AA045320; AA629687; AA932135; H19440; AA916780; H53340; AA932983; AA496283; Hl 5574; AI689831; AA496360; T57841;
H65660; AA683077; AI348319; AA427940; AA442853; AA977679; W46900;
AA676663; AI418741; AA485151; T73468; AA101875; H94487; AI337108;
AI924973; AA490497; H69561; AI301329; R45026; AI569017; AI870821;
T63511; AI925826; AI289185; H79047; AA677432; AA282063; AI989728; AW058317; AA464417; AA487526; AA280692; AA464856; AA987705;
AI675465; AA486324; AI244615; AI214500; AA143331; W72207; AI366840;
AA987658; AA159577; T73558; AI312971; AA634308; AI859300; AI813911;
AI310113; H78386; AA977242; AI521932; AI261741; AI147534; AI924357;
AA477283; AA862966; AA521439; AA857542; AA775223; T86934; AA976699; AA486082; T60163; AA922832; AA485371; R67275; AA400258; AA018683; AI379365; AI536541; AA055163; T65736; AA677706; AA970531; AI418638;
R91078; AA460291; AA504492; H50500; AA423867; AI459325; AI828306;
AI815143; T71349; H95960; AA844831; AW057804; N24824; AA434115;
AA496997; AA625655; AW073502; AI367796; AA461456; AA281635; AI000103; AI364688; AIO 14441; AI253136; AI337294; AW001034; AA430698;
T72076; AA704407; N64384; AA464152; W88655; AA142922; AI888275;
AI337340; AA934734; AA441933; AA872383; AA862465; AI628353; N80129;
AA069024; AI640779; AI241337; AA406601; AA974008; R17765; AA504141;
AI311655; AA598611; AI160214; AA449742; AI016010; R40400; H72028; AA455925; AI982577; AI289110; AI791122; AA086476; AA287550; AA423957;
AI863845; AI261360; AI422367; AW009769; AI986458; AA630800; AI655374;
AA894557; AI924452; AI291863; N70714; N78902; AI333599; AI350508;
AA961735; H73590; AA448478; T53298; AA025150; AI933187; AA923567;
AI241301; AA455369; AI524093; AA490903; AI015711; AI824922; H29295; AI057229; AI261290; AA102454; AA485893; N30096; AA921679; AI432357;
AA857098; A971274; AA989500; AA034939; AI362919; AI148329; T50675;
AA464196; AA863314; AI991902; R99562; AA465233; AI422138; AA133469;
AA682897; AI375353; AW057705; AI150389; AI381043; AA985421;
AA436401; W84701; AA458779; AI823975; AI005513; AA669452; N30372; AW028846; AA970402; AA431347; AA283023; AI366996; AI032392;
AA975430; AA425217; AA974221; W92764; R62603; AI017394; AI005521;
AA280832; AI017442; AI041729; AW006385; AA775957; AI309109;
AA968896; R48303; AI290905; AA947730; AI074272; AI936084; AI023541;
AI356027; AI659370; T94781; AI799888; AA126989; AI356028; AA975832; AA931898; AA456852; AA405569; H72723; AA447079; AI951084; AA894763;
AI394646; AW008766; T62865; AAl 50402; AI423270; AI620493; AA490172;
AI537061; AA453258; AI657057; N71159; AA521345; AI674972; AI884374;
AA446017; AA455261; AI000966; AA931118; AI924306; AA663309; AI470775;
AA991590; AA487193; AI350347; AI652557; AA864299; AA974305; R91950; AA865554; AA999838; AW071052; AA434102; AA521358; W32272; AA911063; AI473897; AA775091; AA857015; AA862436; AI924753; AA430665; T66832; AI301513; AA971278; AI745626; AAOl 1096; AA152347; AA873577; AI269774; AA931491; AI923970; AA857437; AA664009; R91396; AI887514; AW028368; AA029283; AI368479; N68159; AA862371; AI375428; AI473884; T70999; AI368486; AI000474; AA676836; N67017; N53136; AI362740; AI560668; AA029299; AA932696; AI279830; AA476461; AI868227; and AI081269.
Examples
Hereinafter, the present invention will be described in further detail with reference to examples. It is to be understood, however, that these examples are for illustrative purposes only and are not to be construed to limit the scope of the present invention.
Example 1; Preparation of tissue samples and extraction of RNA
Patient's tissues used in all experiments were reviewed and approved for the use thereof by the Internal Review Board of Yonsei University College of Medicine, Seoul, Korea, and particularly, the tissues of patients who underwent surgery in Yonsei University College of Medicine during the period 1997 to 1999 were used with the approval of the patients. Tissues, more than 70% of which consisted of tumors, were stored in liquid nitrogen just after surgery, and a normal gastric tissue sample and a gastric tumor tissue sample corresponding thereto were obtained from the same patient (a pair of samples).
Tissue samples (n=95) were divided into a training set (29 pairs of samples (n=58)), a test set (28 samples; 7 pairs; 8 normal tissue samples and 6 tumor tissue samples) and an independent set consisting of 9 samples (7 tumor tissue samples and 2 normal tissue samples). Before prediction analysis, a tester could be aware of clinical information only on the training sets.
RNA was extracted from homogenized tissue using reagent TRIzol (Invitrogen, Carlsberg, CA) according to a known protocol. The extracted total RNA was further purified using the RNeasy Mini Kit (Qiagen, Valencia, CA).
Example 2: Construction and hybridization of cDNA microarray
The construction and hybridization of a 17K cDNA microarray was performed on the basis of a known protocol (Yang, S.H. et al., Int J Oncol, 22:741, 2003). In other words, the cDNA of normal gastric tissue was isolated and then amplified by PCR. In the PCR amplification, 20 pmol primers, 2.5 mM dNTP mixture, 100 ng plasmid DNA, 5μi 10 x PCR buffer (Solgent Co.) and 2.5 units of Taq DNA polymerase were mixed with each other to make a final volume of 50μJi, and the mixture solution was subjected to a PCR reaction consisting of initial denaturation at 94 °C for 10 minutes, and then 34 cycles of denaturation at 94 °C for 1 minute, annealing at 55 °C for 45 seconds and elongation at 72 °C for 45 seconds, followed by final extension at 72 °C for 10 minutes.
The amplified DNA fragments were purified with the QuiaQuick PCR purification kit (Qiagen) and then electrophoresed on 2% agarose gel to confirm the PCR products. Then, the PCR products were dried, dissolved in 20μi of 50% DMSO solution, and then integrated on a Corning GAPS slide for DNA chips, thus fabricating a chip. Then, the chip was cross-linked by irradiation with 300 mJ UV light and then stored on a drying rack at room temperature.
Cy5-dUTP-labeled cDNA targets of total RNA isolated from the gastric tissue were competitively hybridized with Cy3-dUTP-labeled reference cDNAs derived from a conventional RNA pool (Kim, T.M. et al., CHn. Cancer Res., 11 :79, 2005). Example 3: Analysis of microarray data
FIG. 1 is a general schematic diagram of the present invention and shows an analysis method comprising microarray experiment, gene selection, cross- validation and prediction. GeneSpring 7.0 software (Silicon Genetics, Redwood City, CA) was used to perform normalization of raw data, filtering of genes, classifier gene selection, cross-validation of a training set, and class prediction of a test set and independent set.
Before gene filtering, raw data were subjected to print-tip normalization using the Lowess function (Kim, B.S., Can. Res. Treat., 35:533, 2003). Analysis was continued to remove unclear signals (F532-1.5 XB532O or F635-1.5 X B635O) and also genes having unclear Cy5/Cy3 ratios (13,071 genes remained) were also removed.
The data were filtered by removing genes having low control spots using the Gene Spring "cross-gene error model", thus yielding highly reliable genes (12,891 genes remained). Using one way- ANO VA, genes showing a difference in expression between the normal gastric tissue group and the gastric cancer tissue group were selected (p<0.05: a statistically significant difference). To minimize error rates, p- values were adjusted several times on the basis of the Westfall and Young permutation.
A class prediction function based on the prediction strengths of genes and k-nearest neighbor classification (k=14) was used for cross-validation and prediction. Fisher's exact test and signal-to-noise algorithm were all used to select a small number of predictor genes. For this purpose, predictor genes (894 genes) initially selected from the training set were cross-validated. Through the above procedure, the prediction strengths of all of 894 genes could be measured, and the prediction strengths were evaluated using all gene lists and gene expression data selected within the training set. All the genes were independently evaluated, and hierarchically clustered according to a strength enabling each class to be distinguished from other genes.
The prediction strengths were calculated as the negative natural logarithm of p- values. Based on the prediction strengths, said 894 genes were grouped. In this system, the probability of acquisition of "ideal expression pattern" of each gene in a sample observed from each class (normal or tumor) can be calculated (Golub, T.R., Science, 286:531, 1999).
Instead of using a small number of randomly selected genes, genes based on prediction strength were used as criteria for the selection of a smaller number of genes from 894 genes (FIG. 2). A microarray experiment for 28 samples in the test set and 9 samples in the independent set was carried out separately from the training set, in order to minimize statistical over-fitting in the selection of classifiers. Data normalization and spot quality filtering were carried out in the same manner as in the training set.
Example 4; Selection of class predictor genes, cross-validation in training set, and prediction of tissue type in test set
As described in Example 3 above, 894 genes were selected from 12,891 genes by one-way ANOVA (p<0.05) between the normal tissue and tumor tissue of the training set. Based on a change in the relative expression of these genes, the types of the tissues were accurately predicted in cross-validation for all of 58 samples of the training set, and the accuracy of the prediction was 100%. The same set of 894 genes was used to predict tissue types in the test set, 27 (96.4%) of 28 samples in the test set were accurately predicted. Through the cross-validation and prediction process, the order of 894 genes was determined according to the prediction strength of each gene. The subset of 894 genes was further classified through cross-validation in the training set and was predicted in the test set.
Table 1 below shows 894 genes selected using one-way ANOVA between the normal tissues and tumor tissues of the training set. In Table 1, Avg. Ratio in normal tissueb and tumor tissue0 represents the average of the Cy5/Cy3 ratios of 29 normal tissues in the training set.
Figure imgf000021_0001
Figure imgf000022_0001
Figure imgf000023_0001
Figure imgf000024_0001
Figure imgf000025_0001
Figure imgf000026_0001
Figure imgf000027_0001
Figure imgf000028_0001
Figure imgf000029_0001
Figure imgf000030_0001
Figure imgf000031_0001
Figure imgf000032_0001
Figure imgf000033_0001
Figure imgf000034_0001
Figure imgf000035_0001
Figure imgf000036_0001
Figure imgf000037_0001
Figure imgf000038_0001
Figure imgf000039_0001
Figure imgf000040_0001
Example 5: Unsupervised hierarchical clustering of training set
In order to select class-predictor genes having high reliability, the characteristics and identity of tissues in the training set must be accurate. This selection was performed by the two-way unsupervised hierarchical clustering of 58 tissue samples and the filtering of 12,891 genes (FIG. 3A). The normal tissues and the tumor tissues were divided into two distinct groups according to the expression pattern of the filtered genes. FIG. 3 A is a dendrogram showing the unsupervised hierarchical clustering of 12,891 genes and 29 pairs of gastric tissue samples in the training set. In FIG. 3A, the longitudinal axis indicates the genes, and the horizontal axis indicates the samples. Also, FIG. 3B is a dendrogram showing the two-way hierarchical clustering of 92 genes and 58 samples in the training set. The blue nodes and red nodes at the upper portion of FIG. 3 indicate the normal tissue samples and the tumor tissue samples, respectively, and the blue bars and red bars at the lower portion of the figure indicate the normal tissues and the tumor tissues, respectively. From the color of the right side bars, expression ratio and color could be estimated.
Example 6: 92 class predictor genes differently expressed between normal tissues and tumor tissues
To examine the relative expression patterns of predictor genes in the normal tissues and the tumor tissues, the two-way hierarchical clustering of 92 typical genes in the training set was performed (FIG. 3B). Detailed information, including the systems, general names and average expression ratios of 92 genes, are shown in Tables 2 to 6 below, and genes shown in Table 2 had the highest prediction strength. In Tables below, detailed gene description8 was obtained from DAVID (http://appsl.niaid.nih.gov/david/), SOURCE (http://genome- www5.stanford.edu/cgi-bin/source/sourceSearch) and AceView
(http^/www.ncbi.nlm.nih.gov/IEB/Research/Acembly/), Avg.Ratio and SD of normal tissue represent the average of the Cy5/Cy3 ratios of 29 normal tissues in the training set, and the standard deviation thereof, respectively. Avg. Ratio and SD in tumor tissue0 represent the average of the Cy5/Cy3 ratios of 29 tumor tissues in the training set, and the standard deviation thereof, respectively.
Table 2
Figure imgf000041_0001
Figure imgf000042_0001
Table 3
Figure imgf000042_0002
Figure imgf000043_0001
Table 4
Figure imgf000043_0002
Figure imgf000044_0001
Table 5
Figure imgf000045_0001
Table 6
Figure imgf000045_0002
Figure imgf000046_0001
Example 7: Prediction of morphology of gastric tumor tissues in independent set
In the results of prediction on the test set, one tissue sample was inaccurately predicted. This sample was Y-GC-01-049, which was labeled with tumor tissue, but was inaccurately predicted to be normal gastric tissue. This was a sample paired with Y-GC-01-048 labeled with normal gastric tissue.
Prediction algorithm predicted Y-GC-01-049 as a normal sample with nearly 100% reliability, and in order to make this ambiguous result clear, Y-GC-01-048 and Y- GC-01-049 were additionally subjected to H&E staining. The result of the H&E staining revealed that Y-GC-01-048 was normal gastritis tissue. It was clearly confirmed from the staining result that Y-GC-01-049, which has been labeled with tumor tissue, but has been predicted to be normal tissue by the prediction method based on expression patterns, was normal gastric tissue (FIG. 4). In other words, the sample was predicted to be labeled with gastric tumor tissue, but was labeled with gastritis tissue. From the above result, it could be found that the cross- validation method according to the present invention showed an accuracy of 100%.
An ultimate class prediction test for the utility of predictor genes was carried out in another independent set, not in the training set or test set. For this purpose, an additional set of 9 samples was independently subjected to a microarray experiment, in which only raw data were normalized. A gene set based on the prediction strengths of 10 genes among 894 genes was used to predict the morphology of tissues in a new set without the pre-processing of additional data. The morphology of all tissues in the independent data set was accurately predicted, like each of the prediction sets (Table 7).
From the experiment result, it was found that said 894 genes could collectively predict gastric cancer/normal gastric tissues by only gene expression pattern with an accuracy of 100%. However, for the construction of the most ideal diagnostic microarray, it is preferable to use the smallest possible number of genes while still maintaining a prediction accuracy of 100%. For this purpose, the present inventors performed the prediction of gastric cancer/normal gastric tissues while maintaining genes having high prediction strength and, at the same time, eliminating genes having low prediction strength. As a result, it was found that gastric cancer/normal gastric tissues could be predicted with an accuracy of 100% even when a minimum of only 10 genes having the highest prediction strength of 37.94 was used.
However, it could be observed that, when one or more genes were decreased from the above 10 genes, the prediction strength was lowered below 100%. Accordingly, the present inventors have found that the gene set comprising a minimum of 10 genes having the highest prediction strength has the ability to predict gastric cancer tissue.
Table 7
Figure imgf000048_0001
Example 8: Real-time RT-PCR
In order to validate the effectiveness of the expression ratios obtained in the microarray experiments, the same RNA was subjected to RT-PCR to examine gene expression patterns (Kim, T.M. et al., Clin. Cancer Res,, 11:79, 2005). The RT-PCR reaction was performed using a total of 20μ# including XOμJi of a mixture solution comprising a QuantiTect SYBR Green PCR mixture consisting of 2.5 mM MgCl2 (Qiagen, CA), 2 μi cDNA and 20 pmol oligonucleotide primers, in a Roter Gene 2072D real-time PCR machine (Corbett Research, Sydney, Australia), according to the following conditions: HotstarTaq DNA polymerase activation at 95 °C for 15 minutes; and 30 cycles (amplification) of 20 sec at 95 °C, 30 sec at 50°C and 45 sec at 72 °C .
An amplified fluorescent signal in each specimen was measured at the late extension step of each cycle. To measure the intensity of a signal from each gene, 10-fold serially diluted human genome DNA was used as a control group. A standard curve was drawn by plotting the measured threshold cycle versus the arbitrary unit of PCR product in reaction based on the β-actin gene expression of serially diluted genome DNA. The threshold cycle (Ct) value was determined as the number of cycles when fluorescence exceeded threshold value. A negative control group had no fluorescent signal until the number of cycles reached 35.
Five randomly selected genes of pepsinogen 5 (PGA5, GenBank# R72097), lipase (LIPF, AW058221), thrombospondin2 (THBS2, H38240), rabl5 effector protein (REP15, AIOl 183) and poly (ADP-ribose) polymerase family member 10 (PARPlO, AI002947) were used for the above purpose.
As primers, a forward primer of SEQ ID NO: 1 (5'- GATACGACACTGTCCAGGTT-3 ') and a reverse primer of SEQ ID NO: 2 (5'- CCAGTTCAGACTTCCAGTGT-3') were used for the amplification of PGA5. Also, for the amplification of LIPF, a forward primer of SEQ ID NO: 3 (5'- TCTGTTC AAAACATGTTCCA-3') and a reverse primer of SEQ ID NO: 4 (5- 'TGTGGT AAATAAGATTGGGG-3') were used. Also, for the amplification of THBS2, a forward primer of SEQ ID NO: 5 (5'- ACGAGGACAT AGATGACGAC-3 ') and a reverse primer of SEQ ID NO: 6 (5- TTTACAAATATCACCCCGTC-3') were used. Also, for the amplification of REP15, a forward primer of SEQ ID NO: 7 (5'-AGCTCACCTATTTGCATCAT- 3 ') and a reverse primer of SEQ ID NO: 8 (5 '-CTCTGTAATTGCGACATGAA- 3') were used. Also, for the amplification of PARPlO, a forward primer of SEQ ID NO: 9 (5'-GAGAGGGGCTGGGCTA-S') and a reverse primer of SEQ ID NO: 10 (5'-ATTCAAACAACAGAGCCG-S'; SEQ ID NO: 10) were used. In addition, for the amplification of β-actin, a forward primer of SEQ ID NO: 11 (5'- GGGAATTCAAAACTGGAACGGTGAAGG-B') and a reverse primer of SEQ ID NO: 12 (5'-GGAAGCTTATCAAAGTCCTCGGCCACA-S') were used.
The relative expressions of genes in six tissues (two pairs, one tumor tissue, and one normal tissue) showed a correlation with the gene expression ratios measured in the microarray analysis and the real-time RT-PCR. A correlation coefficient was measured by performing the comparison of expression level between the data measured in the microarray analysis and the data measured in the RT-PCR. The expression patterns of the five genes, measured by the microarray, were completely the same as those of the real-time RT-PCR (FIG. 5A). Furthermore, the comparison of the expression ratios of the five genes showed that the microarray data and the real-time RT-PCR data had a correlation (r2=0.7) therebetween (FIG. 5B).
INDUSTRIAL APPLICABILITY
As described in detail above, the present invention provides the method for selecting genes showing a specific difference in expression between gastric cancer tissue and normal gastric tissue, as well as the probe composition for the diagnosis of gastric cancer, comprising the selected genes, and the microarray for the diagnosis of gastric cancer, comprising the selected genes.
According to the present invention, gastric cancer-specific genes can be selected in a rapid and cost-effective manner compared to the prior methods, including visual gastric cancer tissue examination method and biochemical gastric cancer detection method requiring a long period of time. Also, the use of the microarray having the selected genes fixed thereon allows gastric cancer to be diagnosed in a simple and accurate manner. In addition, when genes, which are overexpressed specifically in gastric cancer tissue, are used, it is possible to screen anticancer drugs to kill gastric cancer cells.
Although the present invention has been described in detail with reference to the specific features, it will be apparent to those skilled in the art that this description is only for a preferred embodiment and does not limit the scope of the present invention. Thus, the substantial scope of the present invention will be defined by the appended claims and equivalents thereof.

Claims

THE CLAIMS What is Claimed is:
1. A method for selecting gastric cancer-specific genes, the method comprising the steps of:
(a) analyzing gene expression profiles of normal gastric tissue and gastric cancer tissue using a cDNA microarray;
(b) dividing the gene expression profile analysis results into a training set, a test set and an independent set; (c) selecting genes differentially expressed in the gastric cancer tissue and the normal tissue from among the genes in the training set using one-way ANOVA;
(d) classifying the selected genes according to the prediction strength thereof; and
(e) validating the prediction accuracy of the selected genes by cross- validation based on a k-nearest neighbor method and real-time PCR analysis.
2. A probe composition for diagnosing gastric cancer, the composition containing 10-200 consecutive oligonucleotides of GenBank ID NO: R72097, 10-200 consecutive oligonucleotides of GenBank ID NO: R71093, 10-200 consecutive oligonucleotides of GenBank ID NO: AA521228, 10-200 consecutive oligonucleotides of GenBank ID NO: R51912, 10-200 oligonucleotide of GenBank ID NO: AI733427, 10-200 consecutive nucleotides of GenBank ID NO: AW058221, 10-200 consecutive oligonucleotides of GenBank ID NO: H38240, 10- 200 consecutive oligonucleotides of GenBank ID NO: AIOOl 183, 10-200 consecutive oligonucleotides of GenBank ID NO: AI002047, and 10-200 consecutive oligonucleotides of GenBank ID NO: AI913412, wherein said consecutive oligonucleotides have a gastric cancer prediction strength of 37.94.
3. The probe composition according to claim 2, which additionally contains 10-200 consecutive oligonucleotides derived from at least one sequence selected from the group consisting of GenBank ID NOs: AI766870, AI672312, AA457710;
AA928660; N63988; AI990922; AA444051; AW072982; AA482119; R56211;
AA669637; AA450009; AI254931; AA279025; AA455157; AA490929;
AA973568; AI469062; AI299356; AA971493; N92901; AA002126; AA416890; AI961583; H39192; AA419460; AA478298; AI889705; H17034; N66177;
AA035384; AA454732; AA987627; AA845156; AI364113; AA983158;
AI225185; AA419488; AA459305; AA922939; AA404486; AI365395; AI291174;
AA459109; AA454652; AA888224; AA152496; AA973703; AI383789; T71879;
AI936324; N62620; AA935508; AA447781; AA287695; AI356106; AA017383; N35888; AI871588; AI365075; AI635529; AA701652; AA991346; AI984082;
AA935273; AI360385; AA725397; AA436152; AA292226; AA025276;
AA446259; AI254559; AA488075; AI438930; AA418674; AI302139; AA488084;
AI299348; AI302661; AI056417; AA911832; AA676471; AA427954; AA453015;
AA913197; AA700876; AI146764; AA916857; AI439171; AA491209; AI739498; AA436479; AI473896; AA496792; AA775616; AA457114; W73874; AA172400;
H60549; R55786; AI925568; AA598865; AI922341; AI493478; AA046430;
AA418104; AI971229; AI382541; AA629591; AA863149; AA489246;
AA857101; AI361897; N33920; AA702193; AI081973; R02085; R47893;
AA457084; AA281929; AA863125; AA088420; AI652076; R05416; AI160757; N39161; AI310461; AA214530; AA913079; AA489587; AI654147; AI654494;
AA669689; AI272002; AA425022; AA700060; AA873499; AI884731; AI337099;
AI337428; AA292410; AI346147; AA426025; AI521155; AA432084; AA911705;
AA427561; AA598478; AW082097; R56149; H17883; AI299378; AA777410;
N81029; AA279396; AI475805; AI950312; AA598610; T96082; T61948; AA149987; AA497002; AI985398; AI318311; AA458878; AA461118; AI862416;
AI361715; AA425450; AI675311; W73790; AI769340; AA452278; AA598965;
AI655392; AA400464; AA988586; AA047567; AW008721; AI681849;
AA455111; AI300810; AA432063; AI492016; AI291184; AA699469; H25917;
AI423445; AA490011; AW072762; AI263104; AA504465; AA278534; AW003596; AI076929; AA489666; AA458634; AI299221; AA887320; AA454588; AA989217; AI668916; R93124; AI216056; T41173; AA775257;
AW025920; AA989521; AI796806; W76339; AA961116; AA971398; N76927;
AI416975; W96155; AI310550; AA402754; AA975556; N30553; AA489100;
AI565424; AA488341; AA211855; AI357590; AA460282; AA280846; AI797648; AI341318; AI674349; AA488618; AA454540; N29639; AA863225; AA936133;
AA680186; AI401608; AI292232; T71976; AA459265; AI299073; AA928017;
AA442092; AI254648; AW073291; AA125872; AA404387; AW073055;
AI131555; W49563; R60301; AA496022; AA485303; AI268697; AA460833;
N95761; AA461157; AA479344; AI624388; N80235; H26184; AI652005; AI380209; AA916327; AI811492; AI016689; AI438958; AI679562; AI267935;
AA857035; R43873; AI261783; AA486261; T70057; AI675714; AI631139;
AA490887; AA933888; N64679; AA412433; H24326; AI146507; R75635;
AI814648; AI814383; AAOl 1414; AA100036; AI301699; AI652207; AI261737;
AI400273; H17615; AI016025; AA505045; AI871665; R40897; AA055486; AI524284; AI350153; AA464590; AI207000; AA830392; AA521373; AA911045;
AA939219; AI927438; H22944; AA779401; AA150828; AA452353; AI361561;
AA927663; AI658727; AI299994; AI676097; AA430540; AA419251; AI340188;
R45009; N72137; AI363203; AI097617; AA156988; T55560; AA196287;
AI016051; AI361422; T71991; AA279337; H15155; AA885869; AI301123; AA479745; AA482286; H65030; AA457051; AA453774; AI318421; AAl 69469;
H96643; AA953560; AW058344; AA505051; T72336; AA863093; H84982;
AI701664; AA207165; W72294; AA504253; R56774; AI363436; AI924634;
AA677534; AI684973; AI253074; AI383503; R85257; AI768615; AI268273;
AA488070; AA775212; AI341604; AI971009; AA932813; W72792; AW057803; AA668470; AA454597; AA485865; AA885835; AI433513; AA464217;
AI341160; AA458965; AA459401; AI421677; AA664101; AA705720;
AA279804; AI262070; AA102526; AA976851; R43532; AI261207; H73234;
AI311391; AA922859; R76099; AA887001; AI340883; AI523637; H62387;
AA916325; AA865464; AA989210; AA488996; AI979290; AA917683; AA857343; AA913127; AI538184; AA873089; AA888213; AA431832; AA934764; AI369830; N70463; AI302205; AA479199; AI301175; AA424833;
H14372; AA935570; AI301681; AA497033; AI369713; AI766746; AA430443;
AW009108; AI056539; AA928708; AA598776; AA504348; AA070226;
AA235332; T65407; AA458982; AA845178; N91385; AA479795; AI418200; AA989497; H57180; T67006; AI611956; AA931930; AI473336; AI739206;
AI336946; AA488406; AI989344; AW050484; AA845167; AA054073;
AA018980; AI016021; AA412053; AI244667; AI393019; AI024088; AI650283;
AI356451; AI858088; AA878040; N59532; AI017670; AA775738; AI758888;
AA155640; AI802786; AA976691; AI261660; AA406020; AI282021; AA932558; N74637; AA988630; R32848; AA037014; AI301815; AI493046; AI986317;
N33214; AA625888; AI017801; AI963860; AA489017; AI309187; AA504259;
AI968672; W46577; N53380; AA864861; AA922800; AA446477; AA932295;
AA923696; W06980; AA857944; AAl 88179; AA676223; AW058504;
AA873159; AA453816; AW075163; AA994760; AA894855; AA459364; AA701655; AA191245; AI369378; AAl 59620; Hl 8932; AI289178; AA136983;
AW072500; AI418753; AI634715; AA131406; T98612; AI611214; AA459012;
AA975820; AW081868; H52119; R89492; H57494; AI342012; AA932564;
AA490606; N63845; AI298493; AA976544; AI701018; R92425; H45668;
AA664180; AW029441; AA939100; N71003; AI057267; AA489045; AI271909; AI830324; N31492; AI418194; AI271987; AI890849; AA954935; AI538192;
AAOO 1449; H77652; AA991889; AA995197; AW075162; AA490466; AA487488;
R31701; AI261377; AA043343; AA279980; AI245812; H23187; AI346878;
AI095381; N62179; AA045320; AA629687; AA932135; H19440; AA916780;
H53340; AA932983; AA496283; Hl 5574; AI689831; AA496360; T57841; H65660; AA683077; AI348319; AA427940; AA442853; AA977679; W46900;
AA676663; AI418741 ; AA485151; T73468; AA101875; H94487; AI337108;
AI924973; AA490497; H69561; AI301329; R45026; AI569017; AI870821;
T63511; AI925826; AI289185; H79047; AA677432; AA282063; AI989728;
AW058317; AA464417; AA487526; AA280692; AA464856; AA987705; AI675465; AA486324; AI244615; AI214500; AA143331; W72207; AI366840; AA987658; AA159577; T73558; AI312971; AA634308; AI859300; AI813911;
AI310113; H78386; AA977242; AI521932; AI261741; AI147534; AI924357;
AA477283; AA862966; AA521439; AA857542; AA775223; T86934; AA976699;
AA486082; T60163; AA922832; AA485371; R67275; AA400258; AA018683; AI379365; AI536541; AA055163; T65736; AA677706; AA970531; AI418638;
R91078; AA460291; AA504492; H50500; AA423867; AI459325; AI828306;
AI815143; T71349; H95960; AA844831; AW057804; N24824; AA434115;
AA496997; AA625655; AW073502; AI367796; AA461456; AA281635;
AI000103; AI364688; AI014441; AI253136; AI337294; AW001034; AA430698; T72076; AA704407; N64384; AA464152; W88655; AA142922; AI888275;
AI337340; AA934734; AA441933; AA872383; AA862465; AI628353; N80129;
AA069024; AI640779; AI241337; AA406601; AA974008; R17765; AA504141;
AI311655; AA598611; AI160214; AA449742; AI016010; R40400; H72028;
AA455925; AI982577; AI289110; AI791122; AA086476; AA287550; AA423957; AI863845; AI261360; AI422367; AW009769; AI986458; AA630800; AI655374;
AA894557; AI924452; AI291863; N70714; N78902; AI333599; AI350508;
AA961735; H73590; AA448478; T53298; AA025150; AI933187; AA923567;
AI241301; AA455369; AI524093; AA490903; AI015711; AI824922; H29295;
AI057229; AI261290; AA102454; AA485893; N30096; AA921679; AI432357; AA857098; A971274; AA989500; AA034939; AI362919; AI148329; T50675;
AA464196; AA863314; AI991902; R99562; AA465233; AI422138; AA133469;
AA682897; AI375353; AW057705; AI150389; AI381043; AA985421;
AA436401; W84701; AA458779; AI823975; AI005513; AA669452; N30372;
AW028846; AA970402; AA431347; AA283023; AI366996; AI032392; AA975430; AA425217; AA974221; W92764; R62603; AI017394; AI005521;
AA280832; AI017442; AI041729; AW006385; AA775957; AI309109;
AA968896; R48303; AI290905; AA947730; AI074272; AI936084; AI023541;
AI356027; AI659370; T94781; AI799888; AA126989; AI356028; AA975832;
AA931898; AA456852; AA405569; H72723; AA447079; AI951084; AA894763; AI394646; AW008766; T62865; AA150402; AI423270; AI620493; AA490172; AI537061; AA453258; AI657057; N71159; AA521345; AI674972; AI884374; AA446017; AA455261; AI000966; AA931118; AI924306; AA663309; AI470775; AA991590; AA487193; AI350347; AI652557; AA864299; AA974305; R91950; AA865554; AA999838; AW071052; AA434102; AA521358; W32272; AA911063; AI473897; AA775091; AA857015; AA862436; AI924753; AA430665; T66832; AI301513; AA971278; AI745626; AAOl 1096; AA152347; AA873577; AI269774; AA931491; AI923970; AA857437; AA664009; R91396; AI887514; AW028368; AA029283; AI368479; N68159; AA862371; AI375428; AI473884; T70999; AI368486; AI000474; AA676836; N67017; N53136; AI362740; AI560668; AA029299; AA932696; AI279830; AA476461; AI868227 and AI081269.
4. An oligonucleotide microarray for the diagnosis of gastric cancer, which has the probe composition of claims 2 or 3 fixed on a substrate.
5. A cDNA microarray for the diagnosis of gastric cancer, which has the following genes fixed on a substrate: GenBank ID NOs: R72097, R71093, AA521228, R51912, AI733427, AW058221, H38240, AIOOl 183, AI002047, and AI913412, wherein said genes have a gastric cancer prediction strength of 37.94.
6. The cDNA microarray according to claim 5, which has at least one gene fixed additionally on a substrate, said gene selected from the group consisting of GenBank ID NOs: AI766870; AI672312; AA457710; AA928660; N63988; AI990922; AA444051; AW072982; AA482119; R56211; AA669637; AA450009; AI254931; AA279025; AA455157; AA490929; AA973568; AI469062; AI299356; AA971493; N92901; AA002126; AA416890; AI961583; H39192; AA419460; AA478298; AI889705; H17034; N66177; AA035384; AA454732; AA987627; AA845156; AI364113; AA983158; AI225185; AA419488; AA459305; AA922939; AA404486; AI365395; AI291174; AA459109; AA454652; AA888224; AA152496; AA973703; AI383789; T71879; AI936324; N62620; AA935508; AA447781; AA287695; AI356106; AA017383; N35888; AI871588;
AI365075; AI635529; AA701652; AA991346; AI984082; AA935273; AI360385;
AA725397; AA436152; AA292226; AA025276; AA446259; AI254559;
AA488075; AI438930; AA418674; AI302139; AA488084; AI299348; AI302661; AI056417; AA911832; AA676471; AA427954; AA453015; AA913197;
AA700876; AI146764; AA916857; AI439171; AA491209; AI739498; AA436479;
AI473896; AA496792; AA775616; AA457114; W73874; AA172400; H60549;
R55786; AI925568; AA598865; AI922341; AI493478; AA046430; AA418104;
AI971229; AI382541; AA629591; AA863149; AA489246; AA857101; AI361897; N33920; AA702193; AI081973; R02085; R47893; AA457084; AA281929;
AA863125; AA088420; AI652076; R05416; AI160757; N39161; AI310461;
AA214530; AA913079; AA489587; AI654147; AI654494; AA669689; AI272002;
AA425022; AA700060; AA873499; AI884731; AI337099; AI337428; AA292410;
AI346147; AA426025; AI521155; AA432084; AA911705; AA427561; AA598478; AW082097; R56149; H17883; AI299378; AA777410; N81029;
AA279396; AI475805; AI950312; AA598610; T96082; T61948; AA149987;
AA497002; AI985398; AI318311; AA458878; AA461118; AI862416; AI361715;
AA425450; AI675311; W73790; AI769340; AA452278; AA598965; AI655392;
AA400464; AA988586; AA047567; AW008721; AI681849; AA455111; AI300810; AA432063; AI492016; AI291184; AA699469; H25917; AI423445;
AA490011; AW072762; AI263104; AA504465; AA278534; AW003596;
AI076929; AA489666; AA458634; AI299221; AA887320; AA454588;
AA989217; AI668916; R93124; AI216056; T41173; AA775257; AW025920;
AA989521; AI796806; W76339; AA961116; AA971398; N76927; AI416975; W96155; AI310550; AA402754; AA975556; N30553; AA489100; AI565424;
AA488341; AA211855; AI357590; AA460282; AA280846; AI797648; AI341318;
AI674349; AA488618; AA454540; N29639; AA863225; AA936133; AA680186;
AI401608; AI292232; T71976; AA459265; AI299073; AA928017; AA442092;
AI254648; AW073291; AA125872; AA404387; AW073055; AI131555; W49563; R60301; AA496022; AA485303; AI268697; AA460833; N95761; AA461157; AA479344; AI624388; N80235; H26184; AI652005; AI380209; AA916327;
AI811492; AI016689; AI438958; AI679562; AI267935; AA857035; R43873;
AI261783; AA486261; T70057; AI675714; AI631139; AA490887; AA933888;
N64679; AA412433; H24326; AI146507; R75635; AI814648; AI814383; AAOl 1414; AA100036; AI301699; AI652207; AI261737; AI400273; H17615;
AI016025; AA505045; AI871665; R40897; AA055486; AI524284; AI350153;
AA464590; AI207000; AA830392; AA521373; AA911045; AA939219;
AI927438; H22944; AA779401; AA150828; AA452353; AI361561; AA927663;
AI658727; AI299994; AI676097; AA430540; AA419251; AI340188; R45009; N72137; AI363203; AI097617; AA156988; T55560; AA196287; AI016051;
AI361422; T71991; AA279337; H15155; AA885869; AI301123; AA479745;
AA482286; H65030; AA457051; AA453774; AI318421; AA169469; H96643;
AA953560; AW058344; AA505051; T72336; AA863093; H84982; AI701664;
AA207165; W72294; AA504253; R56774; AI363436; AI924634; AA677534; AI684973; AI253074; AI383503; R85257; AI768615; AI268273; AA488070;
AA775212; AI341604; AI971009; AA932813; W72792; AW057803; AA668470;
AA454597; AA485865; AA885835; AI433513; AA464217; AI341160;
AA458965; AA459401; AI421677; AA664101; AA705720; AA279804;
AI262070; AA102526; AA976851; R43532; AI261207; H73234; AI311391; AA922859; R76099; AA887001; AI340883; AI523637; H62387; AA916325;
AA865464; AA989210; AA488996; AI979290; AA917683; AA857343;
AA913127; AI538184; AA873089; AA888213; AA431832; AA934764;
AI369830; N70463; AI302205; AA479199; AI301175; AA424833; H14372;
AA935570; AI301681; AA497033; AI369713; AI766746; AA430443; AW009108; AI056539; AA928708; AA598776; AA504348; AA070226;
AA235332; T65407; AA458982; AA845178; N91385; AA479795; AI418200;
AA989497; H57180; T67006; AI611956; AA931930; AI473336; AI739206;
AI336946; AA488406; AI989344; AW050484; AA845167; AA054073;
AA018980; AI016021; AA412053; AI244667; AI393019; AI024088; AI650283; AI356451; AI858088; AA878040; N59532; AI017670; AA775738; AI758888; AA155640; AI802786; AA976691; AI261660; AA406020; AI282021; AA932558;
N74637; AA988630; R32848; AA037014; AI301815; AI493046; AI986317;
N33214; AA625888; AI017801; AI963860; AA489017; AI309187; AA504259;
AI968672; W46577; N53380; AA864861; AA922800; AA446477; AA932295; AA923696; W06980; AA857944; AA188179; AA676223; AW058504;
AA873159; AA453816; AW075163; AA994760; AA894855; AA459364;
AA701655; AA191245; AI369378; AA159620; H18932; AI289178; AA136983;
AW072500; AI418753; AI634715; AA131406; T98612; AI611214; AA459012;
AA975820; AW081868; H52119; R89492; H57494; AI342012; AA932564; AA490606; N63845; AI298493; AA976544; AI701018; R92425; H45668;
AA664180; AW029441; AA939100; N71003; AI057267; AA489045; AI271909;
AI830324; N31492; AI418194; AI271987; AI890849; AA954935; AI538192;
AA001449; H77652; AA991889; AA995197; AW075162; AA490466; AA487488;
R31701; AI261377; AA043343; AA279980; AI245812; H23187; AI346878; AI095381; N62179; AA045320; AA629687; AA932135; H19440; AA916780;
H53340; AA932983; AA496283; Hl 5574; AI689831; AA496360; T57841;
H65660; AA683077; AI348319; AA427940; AA442853; AA977679; W46900;
AA676663; AI418741; AA485151; T73468; AA101875; H94487; AI337108;
AI924973; AA490497; H69561; AI301329; R45026; AI569017; AI870821; T63511; AI925826; AI289185; H79047; AA677432; AA282063; AI989728;
AW058317; AA464417; AA487526; AA280692; AA464856; AA987705;
AI675465; AA486324; AI244615; AI214500; AA143331; W72207; AI366840;
AA987658; AA159577; T73558; AI312971; AA634308; AI859300; AI813911;
AI310113; H78386; AA977242; AI521932; AI261741; AI147534; AI924357; AA477283; AA862966; AA521439; AA857542; AA775223; T86934; AA976699;
AA486082; T60163; AA922832; AA485371; R67275; AA400258; AA018683;
AI379365; AI536541; AA055163; T65736; AA677706; AA970531; AI418638;
R91078; AA460291; AA504492; H50500; AA423867; AI459325; AI828306;
AI815143; T71349; H95960; AA844831; AW057804; N24824; AA434115; AA496997; AA625655; AW073502; AI367796; AA461456; AA281635; AI000103; AI364688; AIO 14441; AI253136; AI337294; AW001034; AA430698;
T72076; AA704407; N64384; AA464152; W88655; AA142922; AI888275;
AI337340; AA934734; AA441933; AA872383; AA862465; AI628353; N80129;
AA069024; AI640779; AI241337; AA406601; AA974008; R17765; AA504141; AI311655; AA598611; AI160214; AA449742; AI016010; R40400; H72028;
AA455925; AI982577; AI289110; AI791122; AA086476; AA287550; AA423957;
AI863845; AI261360; AI422367; AW009769; AI986458; AA630800; AI655374;
AA894557; AI924452; AI291863; N70714; N78902; AI333599; AI350508;
AA961735; H73590; AA448478; T53298; AA025150; AI933187; AA923567; AI241301; AA455369; AI524093; AA490903; AI015711; AI824922; H29295;
AI057229; AI261290; AA102454; AA485893; N30096; AA921679; AI432357;
AA857098; A971274; AA989500; AA034939; AI362919; AI148329; T50675;
AA464196; AA863314; AI991902; R99562; AA465233; AI422138; AA133469;
AA682897; AI375353; AW057705; AI150389; AI381043; AA985421; AA436401; W84701; AA458779; AI823975; AI005513; AA669452; N30372;
AW028846; AA970402; AA431347; AA283023; AI366996; AI032392;
AA975430; AA425217; AA974221; W92764; R62603; AI017394; AI005521;
AA280832; AI017442; AI041729; AW006385; AA775957; AI309109;
AA968896; R48303; AI290905; AA947730; AI074272; AI936084; AI023541; AI356027; AI659370; T94781; AI799888; AA126989; AI356028; AA975832;
AA931898; AA456852; AA405569; H72723; AA447079; AI951084; AA894763;
AI394646; AW008766; T62865; AAl 50402; AI423270; AI620493; AA490172;
AI537061; AA453258; AI657057; N71159; AA521345; AI674972; AI884374;
AA446017; AA455261; AI000966; AA931118; AI924306; AA663309; AI470775; AA991590; AA487193; AI350347; AI652557; AA864299; AA974305; R91950;
AA865554; AA999838; AW071052; AA434102; AA521358; W32272;
AA911063; AI473897; AA775091; AA857015; AA862436; AI924753;
AA430665; T66832; AI301513; AA971278; AI745626; AAOl 1096; AA152347;
AA873577; AI269774; AA931491; AI923970; AA857437; AA664009; R91396; AI887514; AW028368; AA029283; AI368479; N68159; AA862371; AI375428; AI473884; T70999; AI368486; AI000474; AA676836; N67017; N53136; AI362740; AI560668; AA029299; AA932696; AI279830; AA476461; AI868227; and AI081269.
PCT/KR2006/002340 2005-06-17 2006-06-19 Method for selecting gastric cancer specific predictor genes and use thereof WO2006135220A1 (en)

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