KR20120061010A - Method for Diagnosis of Gastric Cancer - Google Patents

Abstract

PURPOSE: A method for diagnosing gastric cancer using soluble truncated c-Met is provided to simply and efficiently diagnose gastric cancer. CONSTITUTION: A method for diagnosing gastric cancer comprises: a step of measuring soluble truncated c-Met protein level in a biological sample; and a step of comparing protein level of the biological sample with the protein level of a normal individual. The biological sample is blood or plasma. The method additionally comprises a step of identifying genetic risk score of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC. A composition for diagnosing gastric cancer contains an agent for measuring soluble truncated c-Met protein level.

Description

Method for Diagnosing Gastric Cancer {Method for Diagnosis of Gastric Cancer}

The present invention relates to a method for diagnosing gastric cancer using soluble truncated c-Met protein.

Helicobacter pylori is a bacterium found in about 50% of the world's humans. It can cause gastric ulcers and severe gastric cancer, and recent studies have shown that those infected with Helicobacter bacillus are producing cytotoxic proteins (CagA). It was found that the risk of gastric cancer was 3.7 times higher than in the other cases.

Therefore, it can be predicted that the protein interacting with CagA is involved in the development of gastric cancer, and among them, c-Met protein, a hepatocyte growth factor (HGF) receptor, has been actively studied.

c-Met protein is a transmembrane protein that is transcribed from the proto-oncogene. Mature c-Met proteins are heterodimer α (50-kDa) and β (145-KDa) chains linked by disulfide bonds (Giordano S et al . , 1988). β-chains link HGF binding domains outside the cell membrane with intracellular tyrosine-kinase active domains (Bottaro DP et al . , 1991, Dean M et al. , 1985).

Upon proteolysis, the entire c-Met protein is separated from the endothelial cell membrane to form a soluble truncated c-Met protein. The soluble truncated c-Met protein consists of the whole cell outer part of the membrane domain and binds HGF competitively with the whole c-Met protein (Wajih N et al., 2002). The selective binding of these soluble truncated c-Met proteins suggests the possibility of forming opposite cell signaling to inhibit cancer development.

While the total c-Met protein can be expressed only in tissues or cells, the soluble incomplete c-Met protein can check its concentration in the blood. Therefore, if the soluble incomplete c-Met is used as a biomarker, the possibility of disease can be easily and efficiently determined, and many studies have been made to use it as a biomarker.

Accordingly, the present invention is to provide a method for easily and efficiently diagnosing gastric cancer by using a soluble truncated c-Met protein as a marker for gastric cancer diagnosis.

The present invention to solve the above problem, measuring the level of soluble truncated c-Met protein in a biological sample; And comparing the protein level with that of a normal individual.

In another aspect, the present invention provides a composition for diagnosing gastric cancer comprising an agent for measuring the level of soluble truncated c-Met protein.

According to the present invention, soluble truncated c-Met protein, which can be easily collected from blood, is used as a marker, and thus gastric cancer can be diagnosed easily and efficiently.

1 is a ROC curve of Example 6.

The present invention comprises the steps of measuring the level of soluble truncated c-Met protein in a biological sample; And comparing the protein level with that of a normal individual.

The inventors of the present invention continue to use the soluble truncated c-Met protein as a marker for diagnosing a disease, and in patients with gastric cancer, soluble truncated in the blood when gastric cancer is neared. The present inventors have found that the c-Met protein concentration is reduced, thus completing the present invention.

In one embodiment of the invention, the biological sample is not limited now, but may specifically use tissue, cells, blood or plasma, preferably blood or plasma. The present invention is useful in that a protein that is easily detected in blood is used as a marker. Blood may be taken, for example, from veins, arteries or capillaries by methods known in the art, and more specifically, blood may be obtained by taking venous blood from the subject's arm via a syringe, but not limited thereto. It doesn't happen.

In another embodiment of the present invention, the measurement of the level of the soluble truncated c-Met protein is performed by a method of contacting a sample with an antibody that specifically recognizes the protein and measuring its antigen-antibody complex formation. Can be. The amount of the antigen-antibody complex formed can be quantitatively measured through the size of a signal of a detection label, and the detection label is not particularly limited thereto, but may be an enzyme, a fluorescent substance, a ligand, a luminescent substance, or a microparticle. At least one selected from the group consisting of redox molecules and radioisotopes may be used.

Methods for measuring protein levels include Western blot, ELISA, radioimmunoassay, radioimmunoassay, oukteroni immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, pricipitin Competitive or non-competitive known analytical methods such as reactions, gel diffusion prispicin response, aggregation assays, fluorescence immunoassays, Protein A immunoassays, FACS, protein chips, and the like can be used, but are not limited thereto. Can be performed.

The ELISA is a direct sandwich ELISA using another labeled antibody that recognizes the antigen in a complex of the antibody and the antigen attached to the solid support, and reacted with another antibody that recognizes the antigen in the complex of the antibody and antigen attached to the solid support. And various ELISA methods such as indirect sandwich ELISA using a labeled secondary antibody which then recognizes this antibody.

In another embodiment of the present invention, the diagnostic method comprises 10 to 50 consecutive DNA sequences comprising a 27th base (polymorphic site) in one or more sequences selected from the group consisting of SEQ ID NOs: 1 to 3 or Identifying the presence of a complementary sequence.

The inventors of the present invention further examined the gene polymorphism present in the gene of c-Met protein, and found that the SNP represented by SEQ ID NO: 1 to SEQ ID NO: 3 is significant with gastric cancer, and it is soluble in truncated c- Analysis with information on the level of Met protein found that the accuracy of gastric cancer diagnosis was higher.

In another embodiment of the present invention, specific nucleotide sequences of the SNPs of SEQ ID NOs: 1 to 3 are shown in Table 1 below.

SEQ ID NO: rs number (ncbi gene bank) Base sequence from 5'- One rs41739 TATGTTAAGCAAAACATACTTTAGAA [A / G] CAAATGAAAAAGGCAATTGAAAATC 2 rs6566 TTGTTCTGATAAATCATGCAATTAAA [A / G] TAAAGTGATGCAACATCTTGTATAC 3 rs41738 GCTACTCTGATCTAATGAATGTGAAC [A / G] TGTAGATGTTTTGTGTGTATTTTTT

The position of the SNP in each of the polynucleotides is the 27th base, and the allelic types that may appear are shown in parentheses.

The presence of the sequence may be obtained by obtaining a nucleic acid sample from a sample; And detecting the presence of the sequence in the sample.

In another embodiment of the present invention, the sequence is 10 to 50 consecutive DNA sequences including the 27th base (polymorphic region) in one or more sequences selected from the group consisting of SEQ ID NOs: 1 to 3 or complement thereof And the 27th base is G for SEQ ID NO: 1; A for SEQ ID NO: 2; In case of SEQ ID NO: 3, it may be G.

In the step of obtaining a nucleic acid sample from the sample, the nucleic acid can be isolated from all cells, such as blood, skin cells, mucosal cells and hair of the diagnostic object. The nucleic acid may be DNA or RNA, preferably DNA. The method of extracting nucleic acid from the cell is not particularly limited, and techniques known in the art or commercially available kits for extracting nucleic acids can be used. For example, phenol / chloroform extraction and protease K treatment can be used.

Kits for DNA or RNA extraction are available from Qiagen, Inc, and Stratagene. In the case of extracting and using RNA from the above, cDNA may be prepared and used through reverse transcription.

In the step of detecting the presence of a gene sequence indicative of the risk of developing gastric cancer in a sample, gene sequence analysis may be performed. Sequencing may use any method known in the art, and the present invention is not limited thereto, but may include, but is not limited to, an autobase sequencer, pyrosequencing, PCR-RELP (restriction fragment length polymorphism), PCRSSCP (single strand conformation polymorphism), PCR-SSO (specific sequence oligonucleotide), PCR-SSO and dot hybridization combined ASO (allele specific oligonucleotide) hybridization, TaqMan-PCR, MALDI-TOF / MS, Any one or more selected from known methods such as RCA method (rolling circle amplification), HRM (high resolution melting) method, primer extension method, Southern blot hybridization method and dot hybridization method can be used.

In another embodiment of the invention, the diagnostic method may further comprise the step of confirming the value of the genetic risk score of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC.

The inventors of the present invention further examined gene polymorphisms present in seven protein genes that bind CagA, such as c-Met protein, and present in seven genes of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC. Each beta (β) value was obtained through statistical analysis on 112 SNPs. Through this, the genetic risk score value was calculated by conventional methods known in the art, and it was found that analyzing the information with the level of soluble truncated c-Met protein increases the accuracy of gastric cancer diagnosis.

112 SNPs present in the seven genes and their beta values are shown in Table 2 below.

Gene CHR SNP CHR position Minor Allele TEST NMISS BETA CRK 17 rs1083 1272047 C ADDITIVE 398 -0.1679 CRK 17 rs11655449 1274465 A ADDITIVE 396 0.05213 CRK 17 rs11657524 1304873 G ADDITIVE 393 0.01803 CRK 17 rs16946807 1303329 A ADDITIVE 394 0.01411 CRK 17 rs2063187 1295739 C ADDITIVE 396 -0.2427 CRK 17 rs7208768 1299451 A ADDITIVE 397 0.0248 CRK 17 rs8073032 1283157 C ADDITIVE 398 -0.1679 CRKL 2 rs1043235 19636400 A ADDITIVE 398 0.14 CRKL 22 rs1043242 19636743 A ADDITIVE 398 0.14 CRKL 22 rs17819409 19632320 C ADDITIVE 395 0.2734 CRKL 22 rs2266953 19632925 C ADDITIVE 391 0.09395 CRKL 22 rs2285547 19637058 C ADDITIVE 398 0.007409 CRKL 22 rs3827296 19610463 G ADDITIVE 398 0.007409 CRKL 22 rs5761368 19607471 A ADDITIVE 398 -0.006822 CRKL 22 rs5761386 19615846 T ADDITIVE 398 0.06582 CRKL 22 rs5761424 19623201 A ADDITIVE 398 -0.5296 CRKL 22 rs737895 19638235 G ADDITIVE 398 0.05092 CSK 15 rs12439525 72874458 T ADDITIVE 387 -0.1131 CSK 15 rs12442901 72870965 A ADDITIVE 396 0.1223 CSK 15 rs12595627 49304392 T ADDITIVE 395 -0.2389 CSK 15 rs1378938 72883496 A ADDITIVE 397 0.1756 CSK 15 rs1378940 72870547 T ADDITIVE 393 0.1631 CSK 15 rs1378941 72867203 T ADDITIVE 392 0.1337 CSK 15 rs1378942 72864420 T ADDITIVE 397 0.1222 CSK 15 rs16972620 72866893 T ADDITIVE 397 -0.1674 CSK 15 rs16972628 72867509 G ADDITIVE 398 -0.1639 CSK 15 rs16972714 72875076 C ADDITIVE 396 -0.08764 CSK 15 rs4886409 72874932 T ADDITIVE 396 -0.1631 CSK 15 rs8039046 72883405 G ADDITIVE 398 -0.1507 GRB2 17 rs7219 70826963 G ADDITIVE 398 -0.04521 GRB2 17 rs8079197 70828274 G ADDITIVE 398 -0.02198 GRB2 17 rs4542691 70838560 T ADDITIVE 398 -0.02198 GRB2 17 rs4789182 70886540 G ADDITIVE 398 -0.02198 GRB2 17 rs7207618 70833282 T ADDITIVE 396 0.01212 MET 7 rs10234854 116131947 A ADDITIVE 394 -0.07646 MET 7 rs10243024 116133839 A ADDITIVE 386 0.01173 MET 7 rs10246585 116131334 C ADDITIVE 395 -0.07926 MET 7 rs10248537 116129863 T ADDITIVE 398 -0.08046 MET 7 rs10271561 116102441 C ADDITIVE 398 0.04925 MET 7 rs10435378 116212729 A ADDITIVE 398 -0.4099 MET 7 rs11767567 116102104 A ADDITIVE 398 0.03149 MET 7 rs12539654 116117219 T ADDITIVE 398 -0.07206 MET 7 rs13223756 116184808 G ADDITIVE 398 -0.08172 MET 7 rs1621 116224842 G ADDITIVE 397 -0.4557 MET 7 rs16945 116225890 C ADDITIVE 397 0.4349 MET 7 rs17138937 116127818 C ADDITIVE 397 -0.0695 MET 7 rs17138943 116129516 A ADDITIVE 398 -0.07206 MET 7 rs17138948 116131546 T ADDITIVE 398 -0.07206 MET 7 rs17138978 116185999 T ADDITIVE 397 -0.4302 MET 7 rs17138983 116189969 G ADDITIVE 394 -0.3971 MET 7 rs183642 116220866 A ADDITIVE 398 0.4054 MET 7 rs193686 116218663 C ADDITIVE 391 -0.7782 MET 7 rs2023748 116223258 A ADDITIVE 398 0.4011 MET 7 rs2073560 116210397 A ADDITIVE 398 -0.07385 MET 7 rs2237708 116124116 C ADDITIVE 398 -0.101 MET 7 rs2237709 116124380 A ADDITIVE 393 -0.00813 MET 7 rs2237710 116124588 G ADDITIVE 395 -0.1668 MET 7 rs2237711 116125233 A ADDITIVE 398 -0.4524 MET 7 rs2237713 116130376 A ADDITIVE 398 -0.4541 MET 7 rs2237717 116192623 T ADDITIVE 397 0.3734 MET 7 rs2283053 116214255 G ADDITIVE 394 -0.05964 MET 7 rs2299433 116118114 T ADDITIVE 397 -0.1387 MET 7 rs2299435 116128557 C ADDITIVE 398 -0.07206 MET 7 rs2299436 116128619 G ADDITIVE 397 -0.0695 MET 7 rs2299437 116128724 A ADDITIVE 398 -0.08764 MET 7 rs2299438 116128990 G ADDITIVE 394 -0.09215 MET 7 rs2299440 116203563 T ADDITIVE 396 -0.06456 MET 7 rs2402118 116215809 A ADDITIVE 398 0.5237 MET 7 rs2896191 116121280 T ADDITIVE 398 -0.07206 MET 7 rs3807996 116164043 T ADDITIVE 398 -0.4053 MET 7 rs3807997 116207794 A ADDITIVE 398 -0.07385 MET 7 rs38841 116107162 G ADDITIVE 397 -0.2667 MET 7 rs38842 116107641 A ADDITIVE 398 -0.2142 MET 7 rs38845 116109038 A ADDITIVE 390 0.04879 MET 7 rs38849 116119775 C ADDITIVE 395 -0.3744 MET 7 rs38851 116130574 C ADDITIVE 397 0.35 MET 7 rs38852 116141761 T ADDITIVE 378 -0.2334 MET 7 rs38854 116144447 T ADDITIVE 398 0.3515 MET 7 rs38855 116145280 A ADDITIVE 396 -0.2586 MET 7 rs38857 116152649 T ADDITIVE 398 -0.4357 MET 7 rs38858 116157523 G ADDITIVE 392 -0.2282 MET 7 rs38859 116166330 C ADDITIVE 396 -0.3689 MET 7 rs39747 116108275 C ADDITIVE 393 0.1678 MET 7 rs39748 116114666 G ADDITIVE 396 0.3431 MET 7 rs40238 116101563 A ADDITIVE 398 0.4913 MET 7 rs40239 116105113 G ADDITIVE 397 0.3535 MET 7 rs41735 116222652 A ADDITIVE 398 0.4011 MET 7 rs41736 116223004 T ADDITIVE 398 0.4011 MET 7 rs41737 116223333 A ADDITIVE 394 0.4021 MET 7 rs41738 116224442 G ADDITIVE 398 0.4314 MET 7 rs41739 116224740 G ADDITIVE 395 0.4693 MET 7 rs41741 116225747 G ADDITIVE 397 -0.3802 MET 7 rs6566 116225654 A ADDITIVE 397 0.4307 MET 7 rs6951311 116221574 A ADDITIVE 392 0.417 MET 7 rs714180 116106238 A ADDITIVE 395 -0.02992 MET 7 rs9641562 116110027 C ADDITIVE 398 0.07375 PTPN11 12 rs4767860 111426736 G ADDITIVE 389 0.04079 PTPN11 2 rs7132778 111425737 C ADDITIVE 391 -0.024 PTPN11 12 rs7958372 111419936 C ADDITIVE 398 0.01438 PTPN11 12 rs2301756 111375159 T ADDITIVE 398 -0.01422 PTPN11 12 rs11066322 111406912 G ADDITIVE 397 -0.0135 PTPN11 12 rs12229892 111407776 A ADDITIVE 394 0.0003514 SRC 20 rs12106024 35412762 G ADDITIVE 397 0.1604 SRC 20 rs12329503 35440545 C ADDITIVE 378 0.3448 SRC 20 rs1570209 35462245 C ADDITIVE 396 0.2177 SRC 20 rs3790150 35448513 G ADDITIVE 389 0.3945 SRC 20 rs6018027 35424206 C ADDITIVE 394 0.05126 SRC 20 rs6018088 35432039 A ADDITIVE 396 0.2543 SRC 20 rs6018199 35443071 G ADDITIVE 398 0.3763 SRC 20 rs6018257 35455953 C ADDITIVE 395 0.2837 SRC 20 rs6094509 35438689 G ADDITIVE 396 0.3731 SRC 20 rs6122566 35468021 G ADDITIVE 398 0.1845 SRC 20 rs6124914 35449657 C ADDITIVE 397 0.3815 SRC 20 rs6124933 35453340 C ADDITIVE 397 0.3221 SRC 20 rs747182 35416303 C ADDITIVE 398 0.2161 SRC 20 rs8126089 35432668 T ADDITIVE 398 0.3094

In another embodiment of the present invention, the step of comparing the protein level with the protein level of the normal individual can be used to compare the protein level in the biological sample of the suspected gastric cancer patient, absolute and relative using a sample of a normal person as a control.

As a result of relative comparison, when the concentration of the soluble truncated c-Met protein of the sample is lower than that of the normal person, the sample can be diagnosed as gastric cancer.

Absolute comparisons indicate that if the concentration of the soluble truncated c-Met protein in the sample is less than 14-15 ng / ml, 14.5-15 ng / ml, 14.6-14.8 ng / ml, or 14.7 ng / ml It can be diagnosed with

In addition to the above results, the presence of 10 to 50 consecutive DNA sequences including the 27th base (polymorphic region) or one or more complementary sequences thereof in the sequence selected from the group consisting of SEQ ID NOs: 1 to 3 , If the base of the polymorphic site is SEQ ID NO: 1; A for SEQ ID NO: 2; In the case of SEQ ID NO: 3, the probability of gastric cancer of the sample is further increased.

In addition, when the genetic risk scores of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC are higher than those of normal individuals, the probability of gastric cancer of the sample is increased. Genetic risk score values can be obtained by averaging the values of each gene, and are coded as 2 for risk-transformation, 1 for heterozygosity, and 0 for non-risk-transformation. Absolutely, if the genetic risk score is 25 or more, preferably 26 or more can be diagnosed as gastric cancer.

In another aspect, the present invention provides a composition for diagnosing gastric cancer comprising an agent for measuring the level of soluble truncated c-Met protein.

In one embodiment of the invention, the agent measuring the protein level may be an antibody specific for the soluble truncated c-Met protein.

The antibody includes all polyclonal antibodies, monoclonal antibodies and recombinant antibodies that specifically bind to soluble truncated c-Met proteins. Antibodies of the invention also comprise functional fragments of the complete form and antibody molecules having two full length light chains and two full length heavy chains such as Fab, F (ab '), F (ab') ₂ and It also includes the form of Fv.

Antibodies that specifically bind to the soluble truncated c-Met protein include both conventional antibodies as well as antibodies that can be prepared by known methods. Specifically, the soluble truncated c-Met protein is injected into the animal, and the soluble truncated c-Met protein is well known in the art, which is a method well known in the art to obtain a serum containing an antibody by collecting blood from the animal. Specific polyclonal antibodies can be prepared. In addition, soluble truncated c-Met protein specific monoclonal antibodies can be prepared using hybridoma methods or phage antibody library techniques.

In another embodiment of the present invention, the gastric cancer diagnostic composition is 10 to 50 consecutive DNA sequences including the 27th base (polymorphic region) in one or more sequences selected from the group consisting of SEQ ID NOs: 1 to 3 or It may further comprise a probe for the detection of a polynucleotide consisting of a complementary sequence or a complementary polynucleotide thereof.

The probe refers to a natural or modified nucleic acid fragment such as RNA or DNA that can be hybridized to mRNA and a specific nucleotide sequence and labeled to confirm the presence or absence of a specific nucleotide. Probes used for hybridization may be prepared in the form of oligonucleotide probes, single stranded DNA probes, double stranded DNA probes, RNA probes, and the like. Conditions suitable for hybridization can be determined by adjusting the temperature, ionic strength (buffer concentration), the presence of compounds such as organic solvents, and the like. Such stringent conditions can be determined differently depending on the sequence being hybridized.

Those skilled in the art can design primer pairs that specifically amplify a specific region of the gene or probes that specifically recognize a specific region of the gene from known marker gene nucleic acid sequences of the present invention and are known in the art. It can be synthesized chemically. In addition, modifications can be made using labels such as radioisotopes, fluorescent molecules, biotin, and the like to provide detectable signals directly or indirectly.

In another embodiment of the present invention, the composition for diagnosing gastric cancer of the present invention may be prepared as various types of gastric cancer diagnostic kits further including components, detection reagents, or devices suitable for each type of kit.

Specifically, 10 to 50 containing a 27th base (polymorphic site) in an agent for measuring the level of soluble truncated c-Met protein and at least one selected from the group consisting of SEQ ID NOs: 1 to 3. In addition to a probe for detecting a polynucleotide consisting of a contiguous DNA sequence or its complementary sequence or a complementary polynucleotide thereof, a kit for diagnosing gastric cancer further includes a test tube or other suitable container, a reaction buffer, and the like.

The invention also relates to antibodies specific for soluble truncated c-Met proteins, and additionally to reagents capable of detecting bound antibodies, such as labeled secondary antibodies, chromophores, enzymes and Provided is a kit for ELISA comprising a substrate thereof.

As another example, there is provided a kit for a protein chip in which an antibody specific for the marker protein of the present invention is arranged at a predetermined position on a substrate and immobilized at a high density. Kits for protein chips may include buffers, labeling reagents, wash buffers, and the like, used for hybridization.

In addition to the kits exemplified above, it includes several types of diagnostic kits that are made of a rapid diagnostic kit capable of detecting the marker of the present invention.

[Example]

Hereinafter, examples will be described in detail to help understand the present invention. However, the following examples are merely to illustrate the content of the present invention is not limited to the scope of the present invention. The embodiments of the present invention are provided to more completely explain the present invention to those skilled in the art.

< Experimental Example >

The following experimental examples are intended to provide experimental examples that are commonly applied to each embodiment according to the present invention.

1. Experimental subject

A patient-control study was performed within the Korean Muti-Center Cancer Cohort (KMCC). From 1993 to 2004, a total of 19,688 applicants were recruited from all over Korea. After the applicant's consent, the applicants' information was collected through individual interviews. Blood and urine samples were also taken.

Gastric cancer incidence, death and additional diagnostic information for all volunteers were collected through the National Death Reports and Health Insurance Records Database or the National Cancer Registry.

To analyze genes encoding proteins that interact with CagA or genes associated with lower signaling pathways activated by CagA, 180 gastric cancer patients and their controls were used, of which 118 patients and their controls The experiment was performed.

2. Availability c- Met  For protein expression In in vitro  Research

Plasma concentrations of soluble c-Met protein were performed by conventional methods using human c-Met (soluble) ELISA kits (Invitrogen Corporation, USA). 5 μl each plasma sample is 495 μl Standard Diluent Diluted with Buffer and used for analysis. All samples were analyzed twice. A standard curve was used for each assay, which was made with a dilution of 50ng / ml standard of purified mouse myeloma-expressing recombinant protein. The concentration of the sample was determined by measuring the absorbance of the standard and the sample at 450 nm with a microplate reader.

For sensitivity, the minimum detectable amount was <0.5 ng / ml. This is obtained by adding the average two standard deviations to the average OD value obtained by analyzing the zero standard 30 times and calculating the corresponding concentration. Moreover, the ELISA The kit was at a concentration of 4,000 to 50,000 pg / ml and was not cross reactive with other species.

The analysis procedure is as follows.

Plasma samples are stored at −70 ° C. after analysis of the baseline until analysis. 495μl Standard Diluent Buffer is added to a tube containing 5 μl of each plasma. After incubation at room temperature for 2 hours, wash the plate Wash 4 times with buffer . 100 μl of biotin-bound anti-Hu c-Met solution is added to the wells. Shake the plate slowly at room temperature for 1 hour. After washing the plate four times, 100 μl of streptavidin bound HRP Working Solution is added to the wells. The plate is incubated at room temperature for 30 minutes and then washed four times. 100 μl of Stabilized Chromogen is added to each well and incubated for 30 min at room temperature. 100 μl of Stop Solution was added to each well and incubated for 30 minutes, and enzyme linked immunosorbent assay (ELISA) was measured using a 450 nm wavelength with a microplate reader.

3. H. pylori  Infection and CagA  Serum reaction

H. pylori Infection and CagA sera were analyzed using Helico Blot 2.1 ^™ , an immunooblot assay. (MP Biomedicals Asia Pacific, Singapore). Helico Blot 2.1 ^TM kits have been reported to have high sensitivity and accuracy

The analysis procedure is as follows.

Diluted 2 ml of Nitrocellulose strip wash After diluting with the buffer , the mixture was incubated on a rocking platform at room temperature for 5 minutes. Plasma samples were stored at −70 ° C. after baseline and before analysis. 2ml Blotting Buffer was added to each well containing 2 μl of patient serum or positive / negative control. After incubation on a rocking platform at room temperature for 1 hour, the plates were diluted 3 ml 2 times wash Washing was performed using a buffer , followed by soaking in the rocking platform for 5 minutes between each wash process. 2 ml of Working conjugate solution was added to the wells and incubated at room temperature for 1 hour. The conjugate was inhaled and washed three times, followed by 2 ml of substrate. solution was added. The plate was shaken at room temperature for 15 minutes and washed three times as above. Each strip has been interpreted by conventional standards and methods known to those skilled in the art.

4. Statistical Analysis

Analysis of patients and controls against soluble c-Met plasma levels using analysis of variance and covariance (ANCOVA) with smoking, H. pylori infection, and CagA serologic risk factors as gastric cancer-related risk factors. The geometric mean was compared. Linear regression analysis was performed to measure changes in c-Met protein concentration over time or with changes in other variables for gastric cancer diagnosis in those enrolled in the cohort. Association of gastric cancer with a single c-Met protein level or association of gastric cancer with c-Met genotype and protein phenotype, using a conditional or unconditional logistic regression model tailored to smoking, H. pylori infection, and CagA serum response The odds ratio (OR) and 95% confidence interval (CI) were measured to determine.

Based on the ROC (Receiver Operation Characteristic) curve calculated c-statistically from the logistic regression model, the soluble c-Met protein optimum cut-off level was determined as a biomarker for gastric cancer diagnosis. Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were also calculated according to various cut-off levels.

Sensitivity and specificity are shown by true-positive rate ROC curves for false-positive rate. AUC-ROC values of 0.90 to 1.0 are the best, 0.80 to 0.89 are good, 0.70 to 0.79 is normal, 0.60 to 0.69 is bad, and 0.50 to 0.59 are unavailable (Greiner M et al . , 2000).

All statistical analyzes were performed using SAS software version 9.2 (SAS Institute, Cary, North Carolina).

< Example  1> 19,688 Korean manifold cancers Cohort  Basic feature

236 study subjects participated in soluble c-Met protein experiments. Age, H. pylori infection, CagA / VacA serometry, smoking / drinking status, and gastric ulcer history were compared between patients and controls. No particular differences were observed according to these variables.

 Basic Features of 19,688 Korean Multicenter Cancer Cohorts Case
(N = 118) Control
(N = 118) OR  (95% CI )
age Mean (SD) 64.2 (± 8.4) 64.0 (± 8.2) NS gender Female 37 (31.4) 37 (31.4) 1.0 (0.6-1.7) H. pylori infection Positive (+) 89 (85.6) 88 (84.6) 1.1 (0.5-2.3) CagA Positive (+) 59 (56.7) 59 (56.7) 1.0 (0.6-1.7) VacA Positive (+) 64 (61.5) 64 (61.5) 1.0 (0.6-1.7) Smoking ^b Ever smokers 81 (68.6) 68 (57.6) 1.6 (0.9-2.7) Drinking alcohol  ^c Ever drinkers 72 (61.0) 72 (61.5) 1.0 (0.6-1.7) Stomach ulcer history Ever smokers 17 (14.8) 16 (14.0) 1.1 (0.5-2.2)

* p <0.05, ** p <0.01

a. The median age is 63.5 years old. Range is 40.0 to 83.0 years old

b. Past smokers are defined as current smokers.

c. Past drinkers are defined as current drinkers.

Of the 118 gastric cancer patients, 106 patients were enrolled in the Korean multi-center cancer cohort prior to the onset of gastric cancer, which were classified as incidence cases. Twelve patients had been diagnosed with gastric cancer prior to enrollment in the Korean multicenter cancer cohort, which was classified as a prevalence case. The detailed cancer history of 118 gastric cancer patients is shown in Table 4 below.

[Table 4] Detailed Gastric Cancer Information for 118 Gastric Cancer Patients

<Example 2> Plasma soluble c-Met protein levels measured

a. Values measured with an ANCOVA model fitted to smoking (yes / no), H. pylori infection (positive / negative), and CagA serum response (positive / negative).

b. 236 patient-control set

c. Limit analysis of 106 challenge groups and their control sets

Table 5 shows the levels of soluble c-Met protein in plasma according to the above major risk factors for gastric cancer. The total median plasma concentration of soluble c-Met protein was significantly reduced in the gastric cancer patient group compared to the control group (12.4 ng / ml mean total group, 13.7 ng / ml control group, p = 0.0294). A restriction analysis of 106 incidence cases and their controls showed higher significance (12.4 ng / ml incidence cases, 13.8 ng / ml in controls, p = 0.0224).

Referring to Table 6, soluble c-Met protein levels were consistent in the patient group despite stratification according to age, sex, H. pylori infection, CagA serum response, and smoking status, which are known to be involved in gastric cancer. Appeared very low.

Interestingly, soluble c-Met protein levels in gastric cancer patients (old age, male, H. pylori positive, CagA seropositive, and smokers) with gastric cancer-related risk factors were significantly lower (p value less than 0.05) compared to normal controls. Appeared.

[Table 6] Stratification analysis according to risk factors related to gastric cancer

a. Values measured with an ANCOVA model fitted to smoking (yes / no), H. pylori infection (positive / negative), and CagA serum response (positive / negative).

b. The median age of patients and controls is 64.0 years and ranges from 35.0 to 83.0 years.

< Example  3> c- following onset of gastric cancer Met  Protein level measurement

If soluble c-Met protein levels can be used as a biomarker for potential gastric cancer diagnosis, c-Met protein levels are measured before and after the onset of gastric cancer, assuming that changes in the concentration of c-Met protein are also associated with gastric cancer progression. It was.

[Table 7] c-Met protein levels measured before and after gastric cancer

a. Values measured with an ANCOVA model fitted to smoking (yes / no), H. pylori infection (positive / negative), and CagA serum response (positive / negative).

b. Calculated using linear regression model.

c. 236 patient-control set

d. Limit analysis of 106 challenge groups and their control sets

Referring to Table 7, soluble c-Met protein levels continued to decrease near the time of onset of gastric cancer (p value = 0.0033 according to trend). The mean concentration of soluble c-Met protein at 1 year before and after diagnosis of gastric cancer in developmental or diseased cases was 11.3 ng / ml, which was the minimum. In contrast, the mean concentration of soluble c-Met protein 5 years before and after the diagnosis of gastric cancer was similar or slightly lower than that of the control group (13.6 ng / ml vs. 13.7 ng / ml).

A limited analysis of 106 cases and their control group showed that the plasma concentration of soluble c-Met protein decreased as the diagnosis of gastric cancer became closer. Soluble c- before and after 1 year of gastric cancer diagnosis Met protein concentration was the lowest 10.9 ng / ml. In addition, soluble c-Met protein concentration of 5 years ago / after gastric cancer diagnosis were almost the same as c-Met protein levels in the control group (13.6 ng / mL vs. 13.8 ng / ml).

< Example  4> Genotype-phenotype association review

Combination of c-Met genotype and protein phenotype may affect gastric cancer risk. Therefore, we analyzed SNPs of three significant c-Met genes with a permutated p-value of less than 0.2. Each SNP was divided into two categories (risk vs. non-risk genotype) according to dominant genotype coding. Referring to Table 8, the highest risk combinations were the risk genotype rs41739 (G at the polymorphic site) and a soluble c-Met protein concentration of less than 14.7 ng / ml (14.7 ng / ml from the optimal cut-off level of the ROC assay). In this case, the risk of gastric cancer was nine times higher (OR = 9.2, 95% CI 2.3-36.9). Similarly, a 10-fold increased odds ratio (OR) for the risk genotype rs6566 (A at the polymorphic site) or rs41738 (G at the polymorphic site) and a soluble c-Met protein concentration of less than 14.7 ng / ml. (OR = 10.2, 95% CI = 1.6-14.0 and OR = 10.2, 95% CI = 2.5-14.2, respectively) and showed a significant association with gastric cancer. The p-value for the trend was less than 0.0001, indicating that the risk of gastric cancer increased linearly with soluble c-Met protein concentration despite the SNP type.

TABLE 8 c-Met genotype ^a -phenotype association review

a. A significant SNP with a permutated p value <0.2 of the seven CagA binding genes in the initial genetic analysis.

b. Values measured with an ANCOVA model fitted to smoking (yes / no), H. pylori infection (positive / negative), and CagA serum response (positive / negative).

c. Optimal cut-off levels were determined by ROC analysis.

< Example  5> Genotype-phenotype association review

To determine the combination of genotype and protein phenotype, we analyzed the genetic risk score and gastric cancer risk of CagA binding proteins CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC.

The results are shown in Table 9 below.

If the genetic risk score for the MET gene itself is greater than 5.75 and the soluble c-Met protein concentration is less than 14.7 ng / ml, an increased odds ratio (OR) of 11.8 times (OR = 11.8, 95% CI = 3.4- 41.1) showed significant association with gastric cancer. In addition, bilateral relations were also significant at the boundary line (P interaction value = 0.0662).

Also, an increase in odds ratio (OR) of 14.4-fold for median genetic risk scores of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC of 25.31 or higher and soluble c-Met protein concentration of 14.7 ng / ml or lower ) (OR = 14.4, 95% CI = 3.7-55.7), and significantly correlated with gastric cancer. In addition, bilateral relations were also significant at the borderline (P interaction value = 0.0581).

< Example  6> Genotype-phenotype association review

Table 10 shows the expected values when soluble c-Met protein levels are used as biomarkers for gastric cancer diagnostics in all (236) subjects.

Sensitivity
(%) Specificity
(%) PPV ^a NPV  ^b AUC - ROC ^c AUC - ROC ^f 6.3 ng Of ml  2.5 96.6 42.9 49.8 0.50 (0.48-0.53) 0.76 (0.67-0.85) ^*** 7.1 ng / ml  4.2 94.9 45.5 49.8 0.50 (0.48-0.53) 0.76 (0.67-0.85) ^*** 8.3 ng / ml 11.0 89.8 52.0 50.2 0.50 (0.47-0.54) 0.76 (0.67-0.85) ^*** 9.1 ng / ml 16.1 84.7 51.4 50.3 0.50 (0.46-0.55) 0.77 (0.69-0.86) ^*** 9.9 ng / ml 24.6 79.7 54.7 51.4 0.52 (0.47-0.58) 0.78 (0.70-0.87) ^*** 11.1 ng / ml 34.7 71.1 54.7 52.2 0.53 (0.47-0.59) 0.78 (0.69-0.86) ^*** 11.5 ng / ml 39.0 69.5 56.1 53.2 0.54 (0.48-0.60) 0.78 (0.70-0.87) ^*** 11.9 ng / ml 43.2 62.7 53.7 46.3 0.53 (0.47-0.59) 0.76 (0.67-0.85) ^*** 12.3 ng / ml 44.9 61.9 54.1 52.9 0.53 (0.47-0.60) 0.76 (0.67-0.85) ^*** 12.7 ng / ml 50.8 59.3 55.6 54.7 0.55 (0.49-0.61) 0.77 (0.68-0.86) ^*** 13.1 ng / ml 55.9 45.2 55.0 55.2 0.55 (0.49-0.62) 0.77 (0.68-0.86) ^*** 13.5 ng / ml 60.2 53.4 56.3 57.3 0.57 (0.51-0.63) ^* 0.78 (0.69-0.87) ^*** 13.9 ng / ml 64.4 50.8 56.7 58.8 0.58 (0.51-0.64) ^* 0.78 (0.69-0.87) ^*** 14.3 ng / ml 67.8 50.0 57.6 60.8 0.59 (0.53-0.65) ^** 0.78 (0.70-0.87) ^*** 14.7 ng Of ml 72.9 49.3 58.5 64.1 0.61 (0.55-0.67) ^** 0.80 (0.71-0.88)  ^*** 15.1 ng / ml 74.6 45.8 57.9 64.3 0.60 (0.54-0.66) ^** 0.79 (0.71-0.87) ^*** 15.5 ng / ml 75.4 44.9 57.8 64.6 0.60 (0.54-0.66) ^** 0.79 (0.71-0.87) ^*** 15.9 ng / ml 77.1 42.4 57.2 64.9 0.60 (0.54-0.66) ^** 0.79 (0.71-0.85) ^*** 16.3 ng / ml 77.1 39.0 55.8 63.0 0.58 (0.52-0.64) ^** 0.79 (0.70-0.87) ^*** 16.7 ng / ml 78.8 33.9 54.4 61.5 0.56 (0.51-0.62) ^* 0.78 (0.70-0.87) ^*** 17.1 ng / ml 81.4 29.7 53.6 61.4 0.56 (0.50-0.61) ^* 0.78 (0.70-0.87) ^*** 17.5 ng / ml 82.2 25.4 52.4 58.8 0.54 (0.49-0.59) 0.77 (0.69-0.86) ^*** 17.9 ng / ml 83.9 24.6 52.7 60.4 0.54 (0.49-0.59) 0.77 (0.69-0.86) ^*** 18.3 ng / ml 84.8 21.1 51.8 58.1 0.53 (0.48-0.58) 0.77 (0.68-0.86) ^*** 18.7 ng / ml 87.3 16.9 51.2 57.1 0.52 (0.48-0.57) 0.76 (0.67-0.85) ^*** 19.3 ng / ml 89.8 16.1 51.7 61.3 0.53 (0.49-0.58) 0.76 (0.67-0.85) ^*** 19.7 ng / ml 94.1 13.6 52.1 69.6 0.54 (0.50-0.58) ^* 0.76 (0.67-0.85) ^*** 20.9 ng / ml 94.1 12.7 51.9 68.2 0.53 (0.50-0.57) 0.76 (0.67-0.85) ^*** 21.7 ng / ml 94.9 12.7 52.1 71.4 0.54 (0.50-0.57) 0.76 (0.67-0.85) ^*** 22.5 ng Of ml 95.8 11.0 51.8 72.2 0.53 (0.50-0.57) ^* 0.76 (0.67-0.85) ^***

* 0.01 = p <0.05, ** 0.0001 = p <0.01, *** p <0.0001

a. Positive predictive value = True positive value

b. Negative predictive value = True negative value

c. AUC-ROC (Area under curve- Receive operating characteristics) Model: c-Met protein

d. AUC-ROC Model: c-Met protein, CagA positivity, H. pylori infection, smoking status and total genetic score of seven CagA binding genes

The optimal cut-off values were determined by considering both the sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) together with the maximum AUC values calculated in the logistic model. When the 14.7 ng / ml soluble c-Met protein level was determined as the optimal cut-off level and only soluble c-Met protein was considered as a variable, the sensitivity, specificity, positive predictive value, and negative predictive value were 2.9% and 49.3, respectively. %, 58.5% and 64.1%. As the AUC-ROC value was 0.61, the sensitivity and specificity were measured poorly.

However, when the genetic risk scores of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC were added as variables, and H. pylori infection, CagA and smoking were considered, the AUC-ROC value was 0.80. ROC curve at this time is as shown in FIG.

Having described the specific parts of the present invention in detail, it will be apparent to those skilled in the art that such specific descriptions are merely preferred embodiments, and thus the scope of the present invention is not limited thereto. will be. Accordingly, the actual scope of the present invention will be defined by the appended claims and their equivalents.

<110> Seoul National University R & D Foundation <120> Method for Diagnosis of Gastric Cancer <160> 3 <170> KopatentIn 1.71 <210> 1 <211> 52 <212> DNA <213> Homo sapiens <400> 1 tatgttaagc aaaacatact ttagaawcaa atgaaaaagg caattgaaaa tc 52 <210> 2 <211> 52 <212> DNA <213> Homo sapiens <400> 2 ttgttctgat aaatcatgca attaaawtaa agtgatgcaa catcttgtat ac 52 <210> 3 <211> 52 <212> DNA <213> Homo sapiens <400> 3 gctactctga tctaatgaat gtgaacwtgt agatgttttg tgtgtatttt tt 52

Claims (10)

Measuring the level of soluble truncated c-Met protein in the biological sample; And comparing the protein level with that of a normal individual.
The method of claim 1,
Said biological sample is blood or plasma gastric cancer diagnostic method.
The method of claim 1,
Further comprising the step of identifying the presence of 10 to 50 contiguous DNA sequences comprising the 27th base (polymorphic site) or complementary sequences thereof, in a sequence selected from the group consisting of SEQ ID NOs: 1-3 How to diagnose stomach cancer.
The method of any one of claims 1 to 3, further comprising the step of identifying the values of the genetic risk scores of CRK, CRKL, CSK, GRB2, MET, PTPN11, and SRC.
The method of claim 1, further comprising determining a gastric cancer patient when the level of soluble truncated c-Met protein in the biological sample is lower than that of a normal individual.
4. The method of claim 3, wherein the level of soluble truncated c-Met protein in the biological sample is lower than that of a normal individual, and the base of the polymorphic site is SEQ ID NO: 1; A for SEQ ID NO: 2; In case of SEQ ID NO: 3, the method for diagnosing gastric cancer further comprises determining that the gastric cancer patient is present.
5. The method of claim 4, wherein the level of soluble truncated c-Met protein in the biological sample is lower than that of normal individuals and the values of genetic risk scores of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC are normal. Higher than the value of the object; Or G if the level of the soluble truncated c-Met protein in the biological sample is lower than that of a normal individual and the base of the polymorphic region is SEQ ID NO: 1; A for SEQ ID NO: 2; In the case of SEQ ID NO: 3, and the value of the genetic risk score of CRK, CRKL, CSK, GRB2, MET, PTPN11 and SRC is higher than the value of the normal individual gastric cancer diagnostic method further comprising the step of judging.
Gastric cancer diagnostic composition comprising an agent for measuring the level of soluble truncated c-Met protein.
The method of claim 8,
A composition for diagnosing gastric cancer, wherein the agent for measuring protein level is an antibody specific for the soluble truncated c-Met protein.
The method according to claim 8 or 9,
A polynucleotide consisting of 10 to 50 consecutive DNA sequences comprising the 27th base (polymorphic region) or a complementary sequence thereof, or a complementary poly-nucleotide thereof, in a sequence selected from the group consisting of SEQ ID NOs: 1 to 3 Gastric cancer diagnostic composition further comprising a probe for the detection of nucleotides.

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