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WO2007015459A1 - Ensemble de gènes servant à la prédiction d’apparition de métastase de noeud lymphatique de cancer colorectal - Google Patents

Ensemble de gènes servant à la prédiction d’apparition de métastase de noeud lymphatique de cancer colorectal Download PDF

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WO2007015459A1
WO2007015459A1 PCT/JP2006/315143 JP2006315143W WO2007015459A1 WO 2007015459 A1 WO2007015459 A1 WO 2007015459A1 JP 2006315143 W JP2006315143 W JP 2006315143W WO 2007015459 A1 WO2007015459 A1 WO 2007015459A1
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lymph node
node metastasis
genes
gene
absence
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Ichiro Takemasa
Takenobu Tasaki
Hikaru Sonoda
Hirofumi Higuchi
Kenichi Matsubara
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University of Osaka NUC
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    • 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
    • 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
    • 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
    • C12Q2600/112—Disease subtyping, staging or classification
    • 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

Definitions

  • the present invention relates to a gene group useful for predicting the presence or absence of lymph node metastasis of colorectal cancer, and a method of utilizing the gene expression information thereof.
  • Colorectal cancer is one of the most advanced molecular biology research capabilities, including the structure of multistage carcinogenesis, and reports on individual genes such as APC, K-ras, p53, and DCC have been reported so far. Many are seen. However, just focusing on one of these genes is not sufficient to express the individuality of colorectal cancer, and in recent years, information on the expression of a large number of genes can be obtained at once by using a DNA microarray or the like. Attempts have been made to obtain useful new knowledge.
  • Non-Patent Document 4 An accurate judgment is made even when test sample data is input to an artificial-Ural network model derived using a part of the random data extracted from the entire data. It has been verified that results can be obtained. Therefore, the artificial-eural network model derived here is generally applied to distinguish four types of cancers belonging to small round blue cell tumors that are not limited to the scope of data in this paper. It is suggested that it is possible. However, the results obtained with the artificial-eural network model are generally unacceptable in that they cannot clearly explain the mathematical basis.
  • Non-patent Document 5 A recent study conducted using a DNA microarray for the purpose of identifying molecular targets involved in liver metastasis of colorectal cancer includes a report by Yanagawa et al. (Non-patent Document 5). The authors performed PCR using a human cDNA as a template, using oligo DNA designed based on the base sequence of human cDNA registered in a public gene database as a primer. An amplified cDNA fragment was obtained. These cDNA fragments are then Using a DNA microarray printed as a template, gene expression profiles of colon cancer primary and colon cancer liver metastases isolated from 10 colon cancer patients were examined.
  • liver metastases As a result, we clarified 40 genes whose expression was increased in liver metastases relative to the primary lesion and 7 genes whose expression was decreased in liver metastases relative to the primary lesion. We identified a set of candidate genes that may be involved in liver metastasis.
  • the DNA microarray method is used to perform statistical analysis processing based on the gene discrimination analysis method on the expression information of genes specifically expressed in colon cancer primary tissue ,
  • a method for identifying a gene set effective in predicting liver metastasis of colorectal cancer, a gene set identified by the method, and expression information of the gene set in colorectal cancer primary tissue is known (Patent Document 1).
  • the gene set and method provide information useful for predicting metachronous liver metastasis of colorectal cancer, and are preferable as a material for identifying an important gene specifically expressed in colorectal cancer.
  • lymph node metastasis of colorectal cancer is completely different from liver metastasis in terms of pathology, these gene sets and methods for colorectal cancer liver metastasis can be directly applied to lymph node metastasis of colorectal cancer. That's not the case.
  • an original DNA microarray was prepared using a probe selected from the cDNA library prepared using the primary cancer tissue of colon cancer, liver metastasis tissue of colon cancer and normal colon mucosa tissue as a material. It has also been shown that it is possible to identify candidate genes that are considered to be related to the development and progression of colorectal cancer by performing gene expression analysis in colorectal cancer tissues using this method (Non-patent Document 6).
  • lymph node metastasis of colorectal cancer as described above, the ability to determine the presence or absence of lymph node metastasis.
  • lymph node metastasis relies on the classic histopathological technique of observation, and such a method for determining lymph node metastasis is not necessarily accurate enough.
  • postoperative adjuvant therapy performed after surgery to remove the primary colorectal cancer can improve the prognosis of patients with lymph node metastasis, but postoperative adjuvant therapy is anorexia and upper abdominal discomfort. ⁇ Some side effects such as nausea may occur, and the quality of life (QOL) and the cost of medical care It is necessary to determine whether it is necessary or unnecessary considering the condition and disease state. Therefore, if a more accurate method for determining lymph node metastasis is found, it can be used as a useful index for decision-making when selecting postoperative adjuvant therapy, and eventually appropriate treatment can be received. This is thought to lead to patient benefit.
  • Patent Document 1 Japanese Patent Application Laid-Open No. 2004-33082
  • Non-Patent Document l Troisi R.J., et al., 1999, Cancer, vol. 85, p. 1670-1676
  • Non-Patent Document 2 Cohen A.M., et al., 1997, Curr Probl Surg., Vol. 34, p. 601-676
  • Non-Patent Document 3 Alizadeh et al., 2000, Nature, vol. 403, p. 503-511
  • Non-Patent Document 4 Khan et al., 2001, Nature Medicine, vol. 7, p. 673-679
  • Non-Patent Document 5 Yanagawa et al., 2001, Neoplasia, vol. 3, No. 5, p.395-401
  • Non-Patent Document 6 Takemasa et al., 2001, Biochem. Biophys. Res. Commun., Vol. 285, p. 1244-1
  • the conventional method for determining the presence or absence of lymph node metastasis of colorectal cancer involves excising a plurality of lymph nodes around the colorectal cancer and observing them under a microscope. There was a problem with accuracy.
  • the present invention aims to provide a method for predicting the presence or absence of lymph node metastasis of colorectal cancer by examining the gene expression profile of the colorectal cancer primary tissue. And In order to make it possible to predict the presence or absence of cancer cell metastasis to lymph nodes, the present invention is based on a set of genes that can be used to determine lymph node metastasis of colorectal cancer and their expression information. The purpose is to provide a discriminant that can be used to determine the presence or absence of lymph node metastasis.
  • the inventors of the present invention have also made efforts in a cDNA library prepared using colon cancer primary tissue, colon cancer liver metastasis tissue and normal colon mucosa tissue as materials. Create an original DNA microarray using the selected probe and use the DNA microarray to obtain gene expression analysis data for the primary colorectal cancer lesion. Through statistical analysis, find a set of genes that can be used to predict the presence or absence of lymph node metastasis, and the discriminant that is used to actually predict the presence or absence of lymph node metastasis based on their expression level. In particular, the present invention has been completed.
  • the present invention provides the following method for selecting a gene set for predicting the presence or absence of lymph node metastasis of colorectal cancer.
  • a method for selecting a gene set for predicting the presence or absence of colorectal cancer lymph node metastasis including the following steps (1) to (4):
  • variable selection method in (4) is a stepwise variable selection method.
  • the present invention also provides a gene set for predicting the presence or absence of the following colon cancer lymph node metastasis: provide.
  • a gene set for predicting the presence or absence of colorectal cancer lymph node metastasis selected by any of the methods 1 to 4 above;
  • NM—003404 (G1592), NM—002128 (G2645), NM—052868 (G3031), NM—005034 (G3177), NM—001540 (G3753), NM—005722 (G3826), and NM—015315 ( G43 70) including the gene represented by the database access number (serial number) above 5
  • the present invention further provides the following method for predicting the presence or absence of lymph node metastasis of colorectal cancer using the selected gene set.
  • a method for predicting the presence or absence of lymph node metastasis of colorectal cancer characterized by using the gene set according to any of 5 or 6 above;
  • genes group analysis methods used in the present invention include the following:
  • oral distri- bution analysis (Logistic Discrimination) can be made.
  • Variable selection methods used in the present invention include the following:
  • the present invention is useful for determining whether or not a colorectal cancer cell is likely to metastasize to nearby lymph nodes when a colorectal cancer patient undergoes a primary colorectal cancer resection operation.
  • a discriminant for predicting the presence or absence of lymph node metastasis is provided based on a series of gene sets and their gene expression information.
  • a favorable lymph node metastasis determination result can be obtained by analyzing the gene expression information of the gene set of the primary colorectal cancer tissue using a mouth dystic regression equation. Therefore, it is possible to predict the presence or absence of cancer cell metastasis to lymph nodes at the time of primary colorectal cancer resection.
  • the method of the present invention is based on a gene set effective for predicting the presence or absence of lymph node metastasis at the time of primary colorectal cancer resection, and the expression level of the gene set! Characterized by a discriminant for predicting the presence or absence of nodal metastasis.
  • a gene set useful for predicting the presence or absence of lymph node metastasis is a comprehensive set of genes that can be used for determination from a comprehensive examination of gene expression in multiple samples of colon cancer primary tissue. It is obtained by selecting.
  • Such comprehensive gene expression analysis methods include microarrays, Northern analysis, ATAC-PCR method (Kato et al., Nuc. Acids Res., Vol. 25, p. 4694— 4696, 1997). And real-time PCR represented by Taq Man PCR (Applied Biosystems), SAGE (Velculescu et al., Science, vol. 270, p. 48) 4-487, 1995) can be used.
  • a DNA microarray More specifically, 63 cases of primary colorectal cancer tissue that had been collected through informed consent and were found to have metastasized to the lymph nodes during histopathological observations during primary lesion removal surgery, Gene expression data were obtained using the above-mentioned DNA microarray for a total of 150 tissues, including 87 primary tumor tissues derived from patients who had no metastasis. As a comparative control, gene expression data obtained from normal colonic mucosal tissue strength around the colon cancer primary tissue for 40 cases was used.
  • the gene expression data described above are based on fluorescence emitted from fluorescently labeled cDNA prepared by hybridizing a fluorescently labeled cDNA prepared using total RNA extracted from cancer tissue force to a DNA microarray and a probe on the DNA microarray.
  • the signal is obtained by detecting and quantifying the signal with a special scanner. A more specific procedure is described below.
  • RNA extraction from colorectal cancer tissue or normal colonic mucosa tissue force is described in the package insert of each reagent using reagents such as TRIzol reagent (GIBC 0 BRL) and ISOGEN (Nitsubon Gene). Can be done according to different methods.
  • the total RNA thus prepared can be used as it is for the preparation of the labeled cDNA described below.
  • a commercially available kit such as mRNA Purification Kit (Amersham Biosciences), purifying polyadenine-added RNA (hereinafter also referred to as “mRNA”) from the total RNA according to the attached method, It can also be used for the preparation of cDNA.
  • Cy3 cDNA A cDNA derived from the primary tumor tissue of colorectal cancer labeled with Cy3 (hereinafter sometimes referred to as “Cy3 cDNA”) is mixed in a mixed solution containing the above total RNA or mRNA, oligo dT primer, dNTP and Cy3 labeled dUTP. After adding reverse transcriptase, it is prepared by warming at 37 to 45 ° C for 1 to 3 hours, preferably at 42 ° C for 1 hour. Preparation of a Cy5-labeled normal colon mucosa-derived cDNA (hereinafter also referred to as “Cy5 cDNA”) used as a comparative control is performed in the same manner using total RNA of normal colon mucosa tissue.
  • Cy5 cDNA a Cy5-labeled normal colon mucosa-derived cDNA
  • Cy3 cDNA and Cy5 cDNA are each heat-treated in a denaturing solution at 65 to 70 ° C for 10 to 20 minutes, preferably at 70 ° C for 10 minutes, neutralized, and then mixed in equal amounts (hereinafter referred to as the following). This mixture is sometimes referred to as “Cy5 'Cy3 cDNA”).
  • a denaturing solution 50 mM EDTA It is possible to use 0.5N NaOH or IN NaOH containing, but it is preferable to use 0.5N NaOH containing 50 mM EDTA.
  • Cy5′Cy3 cDNA is purified using a commercial kit such as Micro con-30 (Amicon) according to the attached method.
  • Hybridization of Cy5'Cy3 cDNA and the probe printed on the DNA microarray is performed as follows. First, in order to heat denature the probe, the DNA microarray was heat-treated, and a hybridization solution containing Cy5'Cy3 cDNA that had been heat-treated at 100 ° C for 2 minutes was added dropwise and covered with a cover glass. Place the array in a sealed container and perform hybridization. As for the hybridization conditions, when the hybridization solution contains formamide, hybridization is performed at 42 ° C for 12 hours or more, and it does not contain formamide. Hybridization takes place at about 68 ° C for over 12 hours.
  • the fluorescence of Cy3 and Cy5 is scanned as image data by scanning the fluorescence of Cy3 and Cy5 with a device such as Scan Array 4000 (GSI Lumonics). Subsequently, by analyzing these image data using microarray data analysis software such as Quantarray software (GSI Lumonics), the fluorescence intensities of Cy3 and Cy5 for all probes are converted into text data. Obtainable.
  • a synthetic DNA having a chain length effective for hybridization is used instead.
  • a synthetic DNA having a length of about 20 nucleotides or more consisting of a part of the sequence is used as a probe and fixed to a glass substrate or the like. It is also possible to use a trick.
  • Cy3ZCy5 which is the ratio of the fluorescence intensity values of Cy3 and Cy5 for each probe, is calculated, converted to a logarithmic value with a base of 2 (hereinafter referred to as “log (Cy3ZCy5)”), and log for each probe.
  • the standardized log (Cy3ZCy5) value can be obtained by subtracting the median.
  • the standardized log (Cy3 / Cy5) value can be used as the expression level of each gene.
  • the standardized numerical data (hereinafter sometimes referred to as “standardized numerical data”) for all cases obtained in this manner is integrated and the probe data containing many missing values is collected.
  • the following selection operation is performed for the purpose of removing from the subsequent analysis target. In other words, only probe data for which data has been acquired in more than 128 cases, or more than 85% of all 150 cases analyzed with the microarray, are selected. This allows you to select only probe data that contains 15% or less missing values.
  • the following selection operations are added to eliminate personal genetic background factors. That is, for each probe, the variance value in the data for 150 primary colon cancer lesions and the variance value in the data for 12 normal colon mucosa were calculated, and the former was 1.1 times the latter. Only the probe data is selected.
  • the average value of all data for cases including missing values to be complemented is the data for all cases of genes containing the missing values.
  • KNN K—Nearest t Neighoors
  • b D Singular Value Decomposition
  • Standardized numerical data (hereinafter also referred to as “standardized gene expression data”) supplemented with missing values prepared by force is not affected by knock ground, and Cy3 and Cy5 Inheritance that does not include errors due to differences in detection sensitivity, does not include missing values, and the variation range of gene expression in the colorectal cancer primary lesion compared to normal colon mucosa is due to individual differences It has gene expression information that exceeds the fluctuation range of child expression, and can ensure the reliability of subsequent statistical analysis.
  • SVM Support Vector Machine
  • the analysis is divided into two groups, one for predicting gene identification and the other for evaluation, to ensure statistical reliability. More specifically, the data of 150 cases were also divided into 42 cases with lymph node metastasis and 57 cases with no lymph node metastasis, 99 cases, and 21 cases with lymph node metastasis and 30 cases with no lymph node metastasis. The data from the former 99 cases are used to identify genes and establish discriminants for predicting the presence or absence of lymph node metastasis. The discriminant is evaluated by discriminating this data. In the following description, the former 99 cases of data used for identification of the predictive gene and establishment of the discriminant are expressed as “training data” and the latter 51 cases used for discriminant evaluation. Data is sometimes expressed as “test data”.
  • the above approach (b) is implemented only for the first two divisions considering a huge amount of calculation.
  • the data for 99 cases for training is divided into 2 1250 times randomly at a ratio of 2: 1, and the principal component analysis and the learning of the -Ural network are repeated using it.
  • rank genes based on their sensitivity to identify the presence or absence of lymph node metastasis. Start with 2121 genes, and continue learning with 1536, 768, 384, 192, 96, 48, and 24 refinements.
  • the number of genes included in each gene set and the correct classification rate of test data using the established discriminant As the average of the number of cases in which the results of Z match the number of test data X 100 (%)), (a) has 144 genes and the correct classification rate is 80.2% (standard deviation is 5.6%), (b For), the number of genes is 192 and the correct classification rate is (90.2%). For (c), the number of genes is 133 and the correct classification rate is 78.6% (standard deviation is 6.2%) and for (d). The number of genes is 138, and the correct classification rate is 86.3% (standard deviation: 4.5%). At this time, 16 types of genes are commonly included in the gene set selected by each approach.
  • the target genes are first set to the above 16 genes, and each of these 16 genes is donated (hereinafter referred to as " In addition to the “main effect”), a statistical analysis is performed that also takes into account the interaction of two genes. As a result, it will be possible to search for a discrimination rule in a wider range including the interaction between genes only by the main effect of individual genes, and it is expected that high discrimination performance can be maintained.
  • CART analysis is performed again with the presence or absence of lymph node metastasis as a response for each of the 100 training data used in the above analysis.
  • rpart of Free software R was used. At that time, the default values were used for all operation parameters. From this analysis, 3 to 5 genes can be obtained per analysis as the number of genes that appear as variables that instruct data division.
  • the discrimination performance of lymph node metastasis by the selected gene set is evaluated by the LOO method. That is, using the remaining 149 sample data excluding one sample, the logistic discriminant including the above six variables is estimated, and the operation of discriminating the sample is divided into 150 samples. To implement. As a result, as shown in Table 2, the correct classification rate for the selected set of genes is estimated to be 88.7% (sensitivity: 77.8%, specificity: 96.6%). As described above, in the present invention, it is possible to clarify a gene set necessary for predicting the presence or absence of lymph node metastasis of colorectal cancer with high accuracy.
  • RNA samples 40 cases of total RNA from normal large intestine mucosa were extracted and mixed to obtain standard normal large intestine mucosa total RNA for use in all experiments.
  • concentrations of these RNA samples were calculated based on the absorbance at a wavelength of 260 nm measured using a spectrophotometer as usual.
  • the fluorescent label target to be hybridized to the DNA microarray was prepared by the following procedure. First, 25 g of colon cancer sample-derived total RNA (hereinafter referred to as “colon cancer RNA”) and 25 ⁇ g of standard normal colon mucosa total RNA (hereinafter referred to as “standard colon mucosa HRNA”) are in separate tubes. 2 g of oligo dT primers each having a force of 18 nucleotides were prepared, brought to a volume of 14 L with sterilized distilled water, heated at 70 ° C. for 10 minutes, immediately transferred to ice and rapidly cooled.
  • colon cancer RNA colon cancer sample-derived total RNA
  • standard colon mucosa HRNA standard colon mucosa total RNA
  • Cy3—dUTP Cy5 labeled dUTP
  • concentration ImM standard colonic mucosa URN
  • the 5 X First Strand Buffer, O.IM DTT and Superscriptll used in this reaction were all purchased from GIBCO BRL.
  • DATP, dCTP, dGTP and dTTP, Cy5-d UTP and Cy3-dUTP, and RNAguard were all purchased from Amersham Biosciences.
  • After the reaction add 5 / z L of denaturing solution (0.5N NaOH, 50 mM EDT A) to each tube, heat at 70 ° C for 10 minutes, and then add 7.5 ⁇ L of 1M Tris-HCl (pH 7. It was neutralized by calorie 5).
  • the colon cancer label target and the standard colon mucosa label target were mixed, and 10 g of human COT-1 DNA (purchased from GIBCO BRL) was added thereto.
  • TE buffer was added to this mixture, adjusted to 500 L, and purified and concentrated using Microcon 30 (purchased from Amicon) to remove unreacted Cy5 dUTP and Cy3-dUTP.
  • the purification / concentration procedure followed the manual attached to Microcon-30. Finally, it was concentrated until the total volume became 5 L, and this was used as a label target to be hybridized to the DNA microarray.
  • the DNA microarray by immersing it in a masking solution (3 g of succinic anhydride, 190 mL of N-methyl-2-pyrrolidone and 21 mL of 0.2 M sodium borate) for 5 minutes, and then in distilled water at 95 ° C.
  • the cDNA printed on the microarray was heat denatured by soaking for 3 minutes. Immediately after that, it was immersed in 95% or more ethanol for 1 minute, dehydrated and air-dried.
  • ScanArray 4000 (GSI Lumonics), a confocal laser scanner dedicated to microarrays, independently scans the fluorescence of Cy3 and Cy5 to hybridize each probe on the microarray. Fluorescence patterns of Cy3 and Cy5 derived from cancer targets and standard colon targets were obtained as 16-bit Tiff scanned image data. Subsequently, these image data are analyzed using QuantArray software (GSI Lumonics), which is analysis software dedicated to microarray data, so that the fluorescence intensity of Cy3 and Cy5 for all probes is numerically expressed in text format.
  • QuantArray software GSI Lumonics
  • the fluorescence intensity value for each probe was subtracted from the fluorescence intensity value of the part where the cDNA was not printed.
  • the portion with a low fluorescence intensity value is greatly affected by experimental errors, other data were rejected, leaving a data point of approximately 3000 forces with a high fluorescence intensity value.
  • the ratio of the fluorescence intensity values of Cy3 and Cy5, ie, Cy3ZCy5 was calculated and converted to a logarithmic value with a base of 2 (hereinafter referred to as “log (Cy3 / Cy5)”).
  • the total log (Cy3 / Cy5) is calculated from the log (Cy3 / Cy5) value of each probe!
  • the standard log (Cy3, Cy5) value was obtained by subtracting the median of the values.
  • the average value of the gene expression level to be complemented by the eight samples that were closest to the sample with the missing value was obtained and used as the complement value for the missing value.
  • the number of samples closest to the sample with missing values is defined as the number that increases the number sequentially and minimizes the root mean square (RMS). All the numerical data obtained by complementing the missing values in this way are hereinafter referred to as standardized gene expression data.
  • a probe printed on a DNA microarray may be referred to as a gene.
  • SVM Support Vector Machine
  • PC A / aNN Prin cipal Component Analysis / artificial Neural Network
  • C Hierarchical Cluster Analysis (HCA) + Stepwise Logistic Discrimination and (d) Classification And Regression Tree (C ART) (Breiman et al., Classification and Regression Trees, Wadswarth, 1983) + Logistic c Discrimination, The following four methods were used.
  • the above data can be divided into two parts 100 times and analyzed for 100 different judgments. After identifying the genes, we selected genes that were identified many times. On the other hand, the approach (b) above was implemented only for the first two splits. However, the data for 99 cases for training were randomly divided into 2 parts at a ratio of 2: 1 1250 times, and the main component analysis and neural network learning were repeated using this. After learning, genes were ranked based on their sensitivity to identify the presence or absence of lymph node metastasis, and the genes were narrowed down. 2121 gene strengths have also begun, and learning has progressed with 1536, 768, 384, 192, 96, 48, and 24 refinements.
  • G1592, G3031, G3826, G4370, G2645, G3177 and G3753 are serial numbers assigned to each probe (gene) on the ColonoChip used in the present invention. And the discriminant for judging the presence or absence of lymph node metastasis is
  • lymph node metastasis using the selected gene set was evaluated by the LOO method did. That is, using the remaining 149 sample data excluding one sample, the logistic discriminant including the above six variables is estimated, and the operation of discriminating the sample by that is performed for each 150 samples. Carried out. The results are shown in Table 2. From Table 2, the correct classification rate for the selected set of genes was estimated to be 88.7% (sensitivity: 77.8%, specificity: 96.6%).
  • lymph node metastasis By the determination of lymph node metastasis enabled by the present invention, it is possible to select a better treatment policy according to the case and to expect a medical economic effect. For example, prognosis can be improved by aggressive treatment for patients with a high possibility of lymph node metastasis, while surgery is recommended for cases with a low possibility of lymph node metastasis. Post-adjuvant therapy can be mild and reduce the physical and economic burden on the patient.

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Abstract

L’invention concerne un procédé de prédiction de présence ou d’absence de métastase de noeud lymphatique de cancer colorectal. Elle concerne également un ensemble de gènes que l’on peut utiliser dans le procédé. Elle porte sur un procédé de sélection d’un ensemble de gènes servant à la prédiction de la présence ou de l’absence de métastase de noeud lymphatique de cancer colorectal, le procédé comprenant les phases suivantes (1) à (4) : (1) analyse des informations concernant l’expression génétique dans une lésion primaire de cancer colorectal chez un patient pour lequel on a diagnostiqué une métastase de noeud lymphatique de cancer colorectal par un examen histopathologie, selon au moins quatre procédés d’analyse impliquant au moins un procédé d’analyse d’apprentissage supervisé, pour ainsi sélectionner un groupe de gènes servant à déterminer la présence ou l’absence de métastase de noeud lymphatique à un taux de classification correct supérieur ou égal à 75% pour chacun des procédés d’analyse ; (2) sélection d’un gène commun choisi dans le groupe de gènes dans tous les procédés d’analyse de la phase (1) ; (3) analyse des informations concernant l’expression génétique ci-dessus pour attribuer la présence ou l’absence de métastase de noeud lymphatique à une combinaison quelconque de deux ou plus de deux gènes puis sélection d’une ou de plusieurs combinaisons de gènes montrant une interaction à partir des combinaisons ; et (4) réalisation d’une sélection variable dans un modèle de régression logistique pour fournir une réponse concernant la présence ou l’absence de métastase de noeud lymphatique en utilisant le gène commun et la ou les combinaisons de gènes comme variables d’explication ; un ensemble de gènes servant à la prédiction de la présence ou de l’absence de métastase de noeud lymphatique de cancer colorectal, que l’on sélectionne selon le procédé ; et un procédé de prédiction de la présence ou de l’absence de métastase de noeud lymphatique de cancer colorectal en utilisant l’ensemble de gènes.
PCT/JP2006/315143 2005-08-01 2006-07-31 Ensemble de gènes servant à la prédiction d’apparition de métastase de noeud lymphatique de cancer colorectal Ceased WO2007015459A1 (fr)

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JP2005222995A JP2007037421A (ja) 2005-08-01 2005-08-01 大腸癌リンパ節転移の有無を予測するための遺伝子セット
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CN113436684A (zh) * 2021-07-02 2021-09-24 南昌大学 一种癌症分类和特征基因选择方法

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