KR102018209B1 - Diagnosis composition of gastric cancer and diagnosis method of gastric cancer using the same - Google Patents

Abstract

The present invention relates to a composition for diagnosing gastric cancer and a method for diagnosing gastric cancer using the composition. In detail, the composition for diagnosing gastric cancer of the present invention may be useful for early treatment of gastric cancer by confirming the expression level of a specific protein in a body fluid, thereby allowing early treatment of gastric cancer, and improving survival of gastric cancer patients.

Description

DIAGNOSIS COMPOSITION OF GASTRIC CANCER AND DIAGNOSIS METHOD OF GASTRIC CANCER USING THE SAME

The present invention relates to a composition for diagnosing gastric cancer and a method for diagnosing gastric cancer using the composition.

Gastric cancer is the fourth most common cancer worldwide, and the number of deaths is the second highest cause of cancer deaths. The incidence of stomach cancer is high in parts of East Asia, Eastern Europe and Latin America. In particular, there are many gastric cancer patients in Asia such as Korea and Japan. Koreans consume twice as much salt per day than Westerners, especially in Korea, Japan, Finland, and Iceland, where they have a habit of eating salted fish. In addition, as a cause of gastric cancer, genetic causes are as important as eating habits. The incidence of gastric cancer has been reported in first-generation offspring of gastric cancer patients. The third cause is the presence of Helicobacter Pylory . Although the causal relationship between Helicobacter pylori infection and gastric cancer is difficult to determine, it is difficult to detect Helicobacter pylori in 40 to 60% of Korean patients with gastrointestinal disease and gastric cancer. Able to know. Therefore, elimination of Helicobacter pylori has emerged as a method of preventing gastritis and gastric cancer.

In general, cancer cells are thought to be caused by the generation of cells that are not proliferated and regulated by a gene having a function of proliferating and regulating normal cells. The condition that cancer cells are localized in the gastric mucosa is classified as early gastric cancer. The prognosis for patients found in early gastric cancer is relatively good. Therefore, early diagnosis and treatment of gastric cancer will contribute to lower mortality from gastric cancer and lower the cost of cancer treatment.

Stomach cancer can range from asymptomatic to severe pain. Most people don't have any early symptoms of gastric cancer, or if they are mild enough, they overlook it, with a slight indigestion or stomach discomfort.

To date, most of the tests for gastric cancer are physical. Gastrointestinal X-rays include dual imaging, compression, mucosal imaging, and gastroscopy. The gastroscope can look directly into the stomach to find small lesions that don't show up on the X-ray, as well as perform biopsies directly in places where gastric cancer is suspected. have. However, there are disadvantages of hygiene problems and suffering during the examination.

The best method for the treatment of gastric cancer is surgical surgery aimed at cure. Although the principle of resection is to cover as wide a range as possible, the extent of resection should be defined taking into account the consequences of surgery following extensive resection. Resection involves not only the stomach but also several organs, so it is difficult to guarantee the prognosis of the patient. In addition, when gastric cancer has metastasized to other organs, it is impossible to undergo radical surgery, so other treatments such as chemotherapy and radiation therapy are taken. The anti-cancer drugs currently available on the market are relieving temporary symptoms or suppressing recurrence after resection and survival. There is only a temporary effect to prolong.

In order to overcome the above problems, the development of a new method for the early diagnosis of gastric cancer is required to research the biomarker of gastric cancer. Specifically, a biomarker composition for diagnosing gastric cancer comprising neurotensin and cholecystokinin and a method for diagnosing gastric cancer using the same are disclosed in Korean Patent Registration No. 10-1438530, and 20 kinds of gastric cancer markers including CKS1B and SKB1 and using the same Gastric cancer diagnostic kit is disclosed in the Republic of Korea Patent Registration No. 10-0645979. The development of agents that measure screening and diagnostic markers of biomarkers that enable accurate determination of the onset and progression of gastric cancer is a major challenge to conquer gastric cancer.

Accordingly, the present inventors are trying to screen biomarkers for the diagnosis of gastric cancer, analyze the expression of protein from gastric cancer patients and normal serum, and using statistical techniques, epidermal growth factor receptor (EGFR), as a biomarker of gastric cancer, TTR (transthyretin), ApoA1 (apolipoprotein A1), RANTES (regulated on activation, normal T cell expressed and secreted), LRG1 (leucine-rich alpha-2-glycoprotein 1) and D.Dimer proteins were selected. In addition, the present invention was completed by confirming that the combination of the above protein markers can be distinguished from gastric cancer patients and normal people with high accuracy when applied to the diagnosis and screening of gastric cancer.

It is an object of the present invention to provide a composition and kit for diagnosing gastric cancer comprising a detection reagent that specifically binds to a selected biomarker.

Another object of the present invention is to provide a method for providing information on the diagnosis of gastric cancer using the composition for diagnosing gastric cancer.

In order to achieve the above object, the present invention provides a composition for diagnosing gastric cancer comprising a detection reagent that specifically binds to a biomarker consisting of EGFR, TTR, ApoA1, RANTES, LRG1 and D.Dimer.

The present invention also provides a kit for diagnosing gastric cancer comprising the composition for diagnosing gastric cancer.

In addition, the present invention provides primers, probes and anti-sense nucleotides that specifically bind to nucleic acid sequences of genes encoding biomarkers consisting of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer. It provides a kit for diagnosing gastric cancer comprising any one or more selected from the group consisting of.

In addition, the present invention 1) measuring the protein expression amount consisting of EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer from the sample; And 2) comparing the protein expression level of step 1) with the protein expression level of a normal individual.

In addition, the present invention comprises the steps of 1) measuring the expression level of a gene encoding a protein consisting of EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer from the sample; And 2) comparing the gene expression amount of step 1) with the gene expression amount of a normal individual.

The composition for diagnosing gastric cancer of the present invention may be useful for early treatment of gastric cancer by confirming the expression level of a specific protein in body fluids, thereby allowing early treatment of gastric cancer, and improving survival of gastric cancer patients.

Hereinafter, the present invention will be described in detail.

The present invention provides a gastric cancer diagnostic composition comprising a detection reagent that specifically binds to a biomarker consisting of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer.

The EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer proteins may be polypeptides consisting of any sequence known in the art. The polypeptide may be a variant or fragment of an amino acid having a different sequence by deletion, insertion, substitution, or a combination of amino acid residues within a range that does not affect the function of the protein. Amino acid exchange in proteins or peptides that do not alter the activity of the molecule as a whole is known in the art. In some cases, it may be modified by phosphorylation, sulfation, acrylation, glycosylation, methylation, farnesylation, or the like.

The detection reagent may be any one or more selected from the group consisting of antibodies, antibody fragments, aptamers and D-peptides. The antibody may be a monoclonal antibody, polyclonal antibody or recombinant antibody. Antibodies can be readily prepared using techniques well known in the art. The antibody fragment may comprise a functional fragment of an antibody molecule. A functional fragment of an antibody molecule refers to a fragment having at least antigen binding function and may be Fab, F (ab '), F (ab') ₂ , Fv, and the like. The aptamer is an oligonucleotide molecule having binding activity to a predetermined target molecule, and may be RNA, DNA, modified oligonucleotide, or a mixture thereof, and may be in linear or cyclic form.

Such monoclonal antibodies can be prepared using hybridoma methods, or phage antibody library techniques, which are well known in the art. In general, hybridoma cells secreting monoclonal antibodies can be made by fusing cancer cells with immune cells isolated from immunologically suitable host animals, such as mice injected with antigenic proteins. These two populations of cell fusion are fused using methods known in the art, such as polyethylene glycol, and the antibody producing cells are propagated by standard culture methods. Subcloning can be performed using limiting dilution and hybridoma cells capable of producing an antibody specific for the antigen after obtaining a uniform cell population can be prepared by culturing in large quantities in vitro or in vivo. Antibodies prepared by the above method can be isolated and purified using methods such as gel electrophoresis, dialysis, salt precipitation, ion exchange chromatography, affinity chromatography and the like.

The polyclonal antibody can be prepared by injecting an immunogen biomarker protein or fragment thereof into an external host according to methods well known in the art. The external host can be a mammal such as a mouse, rat, sheep, rabbit. When the immunogen is injected by intramuscular, intraperitoneal or subcutaneous injection methods, it can be administered with an adjuvant to increase antigenicity. Thereafter, blood may be collected periodically from an external host to obtain serum showing improved titer and specificity for the antigen, from which the antibody may be isolated and purified.

The detection reagent may be a conjugate labeled with a detector such as a chromophore, a fluorescent substance, a radioisotope or a colloid. The chromophore can be peroxidase, alkaline phosphatase or acidic phosphatase, and the fluorescent material can be fluorescein carboxylic acid (FCA), fluorescein isothiocyanate (FITC), fluorescein thiourea (FTH), 7-acetase. Toxycoumarin-3-yl, Fluorescein-5-yl, Fluorescein-6-yl, 2 ', 7'-dichlorofluorescein-5-yl, 2', 7'-dichlorofluorescein-6 -Yl, dihydro tetramethyllosamine-4-yl, tetramethyldamin-5-yl, tetramethyldamin-6-yl, 4,4-difluoro-5,7-dimethyl-4-bora- 3a, 4a-diaza-s-indacene-3-ethyl or 4,4-difluoro-5,7-diphenyl-4-bora-3a, 4a-diaza-s-indacene-3-ethyl , Cy3, Cy5, poly L-lysine-fluorescein isothiocyanate (FITC), rhodamine-B-isothiocyanate (RITC), PE (Phycoerythrin) or rhodamine.

The detection reagent may further include a ligand that can specifically bind to the detection reagent. The ligand may be a conjugate labeled with a detector such as a chromophore, a fluorescent substance, a radioisotope or a colloid, and a ligand treated with streptavidin or avidin.

In addition to the detection reagents described above, the diagnostic composition of the present invention may include distilled water or a buffer to stably maintain their structure.

In a specific embodiment of the present invention, we analyze the expression patterns of proteins from gastric cancer patients and normal serum, and select the EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer proteins, which are biomarkers of gastric cancer, using statistical techniques. It was. In addition, a classification model was created using the combination of the biomarkers, and it was confirmed that the classification model can distinguish gastric cancer patients from normal people with high accuracy (Tables 3 to 6).

The present invention also provides a kit for diagnosing gastric cancer, comprising the composition for diagnosing gastric cancer of the present invention.

The gastric cancer diagnostic composition may have the characteristics as described above. For example, the gastric cancer diagnostic composition may include a detection reagent that specifically binds to a biomarker consisting of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer.

The detection reagent may have the characteristics as described above. In one example, the detection reagent may be any one selected from the group consisting of antibodies, antibody fragments, aptamers and D-peptides. The detection reagent may be bound to a solid substrate to facilitate subsequent steps such as washing or separation of the complex. As the solid substrate, synthetic resin, nitrocellulose, glass substrate, metal substrate, glass fiber, microspheres or microbeads may be used. In addition, as the synthetic resin, polyester, polyvinyl chloride, polystyrene, polypropylene, PVDF or nylon may be used.

The kit distinguishes whether the test subject is a gastric cancer risk group, and enables a medical practitioner such as a doctor to diagnose gastric cancer, as well as monitor the patient's response to the treatment and change the treatment according to the result. It can also be used to identify compounds that modulate the expression of one or more markers in vivo or ex vivo in a gastric cancer model such as mouse and rat.

The gastric cancer diagnostic kit may be prepared by a conventional manufacturing method known to those skilled in the art, and may further include a buffer, a stabilizer, an inactive protein, and the like.

The kit is a high-throughput screening method using a fluorescence method performed by detecting a fluorescence by attaching a fluorescent material to a detector to detect an amount of a detection reagent or a radiation method performed by detecting a radiation by attaching a radioisotope to the detector. , HTS) system, surface plasmon resonance (SPR) method for measuring the plasmon resonance change of the surface in real time without labeling of the detector, or surface plasmon resonance imaging (SPRI) method for imaging and confirming the SPR system.

In a specific embodiment of the present invention, we analyze the expression patterns of proteins from gastric cancer patients and normal serum, and select the EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer proteins, which are biomarkers of gastric cancer, using statistical techniques. It was. In addition, a classification model was created using the combination of the biomarkers, and it was confirmed that the classification model can distinguish gastric cancer patients from normal people with high accuracy (Tables 3 to 6).

In addition, the present invention provides primers, probes and anti-sense nucleotides that specifically bind to nucleic acid sequences of genes encoding biomarkers consisting of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer. It provides a kit for diagnosing gastric cancer comprising any one or more selected from the group consisting of.

The EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer proteins may have the characteristics as described above. The primer is complementary to the gene encoding the biomarker found in the present invention, any primer designed to amplify it can be used.

As used herein, the term “probe” refers to a nucleic acid fragment that is short to several hundred bases capable of specific binding with DNA or RNA, and is used to identify the presence or absence of specific DNA or RNA. Can be. The probe may be prepared in the form of oligonucleotide probe, single-stranded DNA probe, double-stranded DNA probe, RNA probe, etc., and may be labeled with biotin, FITC, rhodamine, digoxigenin, or the like, or labeled with radioisotopes. have.

The antisense nucleotides can be those having double or single stranded DNA, double or single stranded RNA, DNA / RNA hybrids, DNA and RNA analogues and base, sugar or backbone modifications.

The gastric cancer diagnostic kit may have the characteristics as described above.

In a specific embodiment of the present invention, we analyze the expression patterns of proteins from gastric cancer patients and normal serum, and select the EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer proteins, which are biomarkers of gastric cancer, using statistical techniques. It was. In addition, a classification model was created using the combination of the biomarkers, and it was confirmed that the classification model can distinguish gastric cancer patients from normal people with high accuracy (Tables 3 to 6).

In addition, the present invention 1) measuring the protein expression amount consisting of EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer from the sample; And 2) comparing the protein expression level of step 1) with the protein expression level of a normal individual.

The sample of step 1) may include a biological sample capable of identifying a disease-specific polypeptide that can be distinguished from a normal state such as urine, blood, serum or plasma. Specifically, it may be blood, serum or plasma which is a biological liquid sample. In one embodiment of the invention, the sample may be serum.

Samples may be pretreated using methods such as anion exchange chromatography, affinity chromatography, size exclusion chromatography, liquid chromatography, continuous extraction, centrifugation or gel electrophoresis. In one embodiment of the present invention, the sample may be pretreated by centrifugation.

The expression level of step 1) may be measured by any one or more methods selected from the group consisting of Western blot, enzyme-immunochemical detection (ELISA), immunohistochemical staining, immunoprecipitation and immunofluorescence. At this time, the expression level of EGFR, TTR, ApoA1 and RANTES protein is reduced compared to the control, the expression level of LRG1 and D. Dimer protein may be increased compared to the control.

In a specific embodiment of the present invention, we analyze the expression patterns of proteins from gastric cancer patients and normal serum, and select the EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer proteins, which are biomarkers of gastric cancer, using statistical techniques. It was. In addition, a classification model was created using the combination of the biomarkers, and it was confirmed that the classification model can distinguish gastric cancer patients from normal people with high accuracy (Tables 3 to 6).

In addition, the present invention comprises the steps of 1) measuring the expression level of a gene encoding a protein consisting of EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer from the sample; And 2) comparing the gene expression amount of step 1) with the gene expression amount of a normal individual.

The sample of step 1) may include a biological sample capable of identifying a disease-specific nucleotide that can be distinguished from a normal state such as urine, blood, serum or plasma. Specifically, it may be blood, serum or plasma which is a biological liquid sample. In one embodiment of the invention, the sample may be serum.

The expression level of step 1) can be measured by reverse transcription polymerase chain reaction or real time polymerase chain reaction. At this time, the expression level of EGFR, TTR, ApoA1 and RANTES protein is reduced compared to the control, the expression level of LRG1 and D. Dimer protein may be increased compared to the control.

In a specific embodiment of the present invention, we analyze the expression patterns of proteins from gastric cancer patients and normal serum, and select the EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer proteins, which are biomarkers of gastric cancer, using statistical techniques. It was. In addition, a classification model was created using the combination of the biomarkers, and it was confirmed that the classification model can distinguish gastric cancer patients from normal people with high accuracy (Tables 3 to 6).

Hereinafter, the present invention will be described in detail by the following examples.

However, the following Examples are only for illustrating the present invention, and the contents of the present invention are not limited thereto.

Serum obtained

First, sera were obtained from 458 healthy subjects (277 males and 181 females) at Seoul National University Hospital, and 80 and 20 gastric cancer patients from Keimyung University Biobank and Pusan National University Human Resource Bank, respectively. Persons, 34 women). Peripheral blood was collected from the normal person or gastric cancer patient 5ml into a Vacutainer SST II tube (Becton Dickinson), which was left at room temperature. After 1 hour, the tube was centrifuged at 3000 g for 5 minutes and the supernatant was taken to obtain serum and the obtained serum was stored at -80 ° C until use.

Quantification of Protein

Among the more than tens of thousands of proteins present in the human body, 120 protein marker candidates were first selected to select gastric cancer biomarker candidates, and clinical significance, ease of analysis, and algorithm accuracy through two-dimensional electrophoresis and SELDI-TOF MS analysis. 50 markers were selected from the 120 protein marker candidate groups in consideration of the cost, clinical condition, and the like. Since then, 350 patients with gastric cancer and 400 normal patients were validated through statistical analysis of the results. Finally, ApoA1, ApoA2, AFP, CEA, CA125, CA19-9, TTR, B2M, and CRP were identified as candidates for gastric cancer biomarkers. , 18 proteins such as CYFRA21-1, VDBP, ApoA4, PAI-1, sVCAM-1, RANTES, EGFR, LRG1, and D.Dimer were selected, and for the statistical analysis of Example 1 Serum samples were used to quantify the 18 proteins.

<2-1> antibody, Kit  And standard proteins

Proteins of ApoA1, ApoA2, AFP, CEA, CA125, CA19-9, TTR, B2M, CRP, CYFRA21-1, VDBP, ApoA4, PAI-1, sVCAM-1, RANTES, EGFR, LRG1 and D.Dimer Quantification was carried out using the kits or antibodies described in 1 and 2. For the standard protein, RANTES protein was purchased from PeproTech, sVCAM-1, EGFR and LRG1 proteins from R & D Systems, PAI-1 from Calbiochem, VDBP from Biodesign, and ApoA4 was manufactured and used. It was. For lead and calibrator, Sekisui for ApoA1 and ApoA2 proteins, Siemens for TTR and CRP proteins, CEA, CYFRA21-1, B2M, AFP, CA125, D.Dimer and CA19 The -9 protein was purchased from Roche.

Where to Buy Antibodies, Kits, and Standard Proteins Biomarker Manufacturer of Standards Corresponding Antibody Manufacturer 1 Corresponding Antibody Manufacturer 2 RANTES PeproTech R & D Systems R & D Systems sVCAM-1 R & D Systems R & D Systems R & D Systems EGFR R & D Systems R & D Systems R & D Systems LRG1 R & D Systems R & D Systems R & D Systems ApoA4 self-production Abcam self-production PAI-1 Calbiochem Innovative Research USBiological Life Sciences VDBP Biodesign Thermofire Thermofire

Where to Buy Antibodies, Kits, and Standard Proteins Biomarker Medicine Calibrator manufacturer ApoA1 Apo A-1 AutoN "Daiichi" Apo auto N Daiichi Sekisui ApoA2 Apo A-2 AutoN "Daiichi" Apo auto N Daiichi Sekisui TTR N Antiserum to human PreAlbumin N Protein standard SL Siemens CEA Elecsys CEA CEA CalSet Roche CYFRA21-1 Elecsys CYFRA 21-1 CYFRA 21-1 CalSet Roche B2M Tina-quant β2-microglobulin β2-Microglobulin Calibrator Roche CA125 Elecsys CA 125 Ⅱ CA 125 Ⅱ CalSet Roche CA19-9 Elecsys CA 19-9 CA 19-9 CalSet Roche CRP CardioPhase hsCRP N Rheumatology standard SL Siemens AFP Elecsys AFP AFP CalSetⅡ Roche D.Dimer D-DI2 D-Dimer Gen. 2 Calibrator set Roche

<2-2> EGFR  And LRG 1 Quantification of Protein

Serum concentrations of EGFR and LRG1 proteins are known as enzyme-linked immunosorbent assays, in which antibodies label enzymes to antigens and measure enzyme activity to quantitatively measure the intensity and amount of antigen-antibody reactions. , ELISA) method. Detection antibodies of EGFR and LRG1 proteins were labeled with biotin and used for ELISA.

Specifically, 400 μg of anti-human-EGFR antibody (R & D systems, US) or anti-human-LRG1 antibody (R & D systems, US) were first placed in 800 μl PBS solution to biotinylate EGFR and LRG1 detection antibodies. Then, 10 mM EZ-Link Sulfo-NHS-LC-Biotin (Sulfosuccinimidyl-6- [biotin-amido] hexanoate, ThermoFisher Scientific, US) solution was added so that the molar ratio of the solution was 20 times that of the antibody. The reaction was carried out for a minute. When biotin labeling was completed, dialysis was repeated three times by adding 1 L of PBS solution, which was stored at -80 ° C.

Quantification of EGFR and LRG1 proteins was performed by capture antibody (R & D systems, US) or human EGFR against human LRG-1 at a concentration of 1 μg / ml in each well of 96-well microplates (Nalgene Nunc Inc., US). 100 μl of antibody (R & D systems, US) was added and pretreated at 4 ° C. overnight. The next day, the wells were washed three times with PBS containing 0.05% Tween 20, and then the PBS solution containing 5% skim milk was placed in the wells and stirred at room temperature for 2 hours to block nonspecific binding. After washing the wells three times with PBS containing 0.05% Tween 20, 100 μl of serum of EGFR and LRG1 standard proteins, normal and gastric cancer patients obtained in Example 1, were added to each well and allowed to react at room temperature for 1 hour. After washing three times. The biotin-labeled detection antibody prepared by the above method was treated in each well at a concentration of 0.15 μg / ml and reacted for another hour at room temperature. After the reaction, the reaction was washed three times, and then reacted for 30 minutes at room temperature by adding 0.5 g / ml of straptavidin-horseradish-peroxidase (Sigma-Aldrich, US). When finished, washed five times. In order to induce the color reaction, TMB (tetramethylbenzidine, KPL, US) was added to each well 100 μl, and after 15 minutes 2 N sulfuric acid was added to each well 50 μl to stop the reaction. The absorbance was then measured at 450 nm using a microplate reader (Emax, Molecular Devices LLC., US). The measured values were analyzed by 5-parametric-curve fitting using SoftMax Pro software (Molecular Device).

<2-3> RANTES, sVCAM-1, ApoA4, PAI-1, and VDBP  Quantification of Protein

 Serum concentrations of RANTES, sVCAM-1, ApoA4, PAI-1 and VDBP proteins were determined by multiplex immunoassay using the xMAP technology platform (Luminex Corp. US). The microspheres were used in the immunoassay after binding the capture antibody by carbodiimide method and minimize the exposure of the microspheres to light in the whole process.

Specifically, the microsphere suspension of MagPlex (Luminex Corp.) was mixed by vortex and then suspended in a sonic vessel (Sonicor Instrument Corporation, US) for 20 seconds. 1 × 10 ⁶ microspheres were transferred to a microtube, separated using a magnet, the solution was removed, washed with 100 μl of distilled water, and then resuspended in 80 μl of 0.1 M sodium phosphate buffer (pH 6.2). It was. Thereafter, 50 mg / ml of N-hydroxy-sulfosuccinimide (ThermoFisher Scientific, US) and 1-ethyl-3- (3-dimethylaminopropyl) -carbodiimide hydrochloride (1 10 μl each of -ethyl-3- (3-dimethylaminopropyl) -carbodiimide hydrochloride, ThermoFisher Scientific, US) was added thereto, followed by reaction at room temperature for 20 minutes. At this time, the reactants were mixed at 10 minute intervals. The microspheres were washed twice with 250 μl 50 mM MES pH 5.0 and then resuspended in 100 μl 50 mM MES pH 5.0. The final volume was added to the carboxyl-activated microspheres by adding 10 μg of anti-RANTES, anti-sVCAM-1, anti-ApoA4, anti-PAI-1 or anti-VDBP antibody, and 50 mM MES solution. After 500 μl, it was mixed for 2 hours at room temperature. After completion of the antibody binding reaction, the microspheres were washed twice with 500 μl of PBS-TBN (PBS containing 1% BSA, 0.02% Tween 20 and 0.05% sodium azide) and counted by a hemocytometer. Antibody-bound microspheres were suspended in 500 μl PBS-TBN solution at a concentration of 1 × 10 ⁶ and stored at 2-8 ° C. in the dark.

The RANTES and sVCAM-1 proteins were quantified in multiple assays, simultaneously measured in one well, while the ApoA4, PAI-1 and VDBP proteins were quantified in a single assay, measuring only one protein in one well. Specifically, 20 μl of the serum of normal and gastric cancer patients obtained in Example 1 and 20 μl of the microsphere mixture mixed with the capture antibody against RANTES, sVCAM-1, ApoA4, PAI-1 or VDBP protein prepared by the above method were mixed. After 20 microliters of RANTES, sVCAM-1, ApoA4, PAI-1 or VDBP standard protein, or the serum-containing microspheres were added to each well of a 96-well microplate, and reacted at room temperature for 1 hour. . Thereafter, 20 μl of biotin-labeled detection antibody and 20 μl of Streptavidin-R-Phycoerythrin (Jackson ImmunoResearch) were sequentially added and reacted for 1 hour and 30 minutes, respectively. The reaction plate was washed twice with 200 μl of PBST (PBS containing 0.05% Tween 20) solution and microplate cleaner (HydroFlex ™, TECAN, Switzerland) per well and 100 μl of PBST (0.05) The intensity of fluorescence was measured by Luminex TM200 by resuspending in PBS) solution containing% Tween 20. RANTES, sVCAM-1, ApoA4, PAI-1 and VDBP protein concentrations in serum samples were analyzed by five parameter curve fitting using Beadview Software (Upstate, US).

<2-4> Quantification of ApoA1, ApoA2, TTR, CEA, CYFRA21-1, B2M, CA125, CA19-9, CRP, D.Dimer and AFP Proteins

ApoA1, ApoA2 and B2M proteins were measured by immunoadjuvant method using Hitachi 7080 (Hitachi Medical Corp., Japan) equipment according to the manufacturer's protocol. TTR and CRP proteins were measured by immunoadjuvant method according to the manufacturer's protocol using the BN2 System (Siemens AG., Germany) equipment. CEA, CYFRA21-1, CA125, CA19-9 proteins were cobas e601 (Hoffmann-La Roche AG., Switzerland) equipment, and D. Dimer proteins were prepared using the Roche c501 (Hoffmann-La Roche AG., Switzerland) equipment. It was measured by electrochemical emission immunoassay according to the protocol.

Stomach cancer Biomarker  Statistical analysis

The 18 markers (ApoA1, ApoA2, AFP, CEA, CA125, CA19-9, TTR, B2M, CRP, CYFRA21-1, VDBP, ApoA4, PAI-1, sVCAM-1, RANTES, EGFR, LRG1 and D.Dimer The significance of the difference in expression level between gastric cancer patients and normal subjects was determined by t-test. A log ₁₀ transformation was performed on the experimental value obtained in Example 2, and the value (measurement data) was used for statistical analysis. As a result of t-test, if the p-value was 0.1 or less, it was determined as a significant marker.

Significance of the Marker t df P-value ApoA1 -12.341 134.259 0.000 ApoA2 -9.774 122.542 0.000 AFP -1.207 122.213 0.230 CEA 2.428 125.119 0.017 CA125 -0.783 145.461 0.435 CA19-9 1.903 117.975 0.059 TTR -12.520 116.145 0.000 B2M 0.591 132.915 0.555 CRP 4.228 131.349 0.000 CYFRA21-1 0.270 134.860 0.788 VDBP -4.274 141.284 0.000 ApoA4 -0.465 101.463 0.643 PAI-1 -1.815 125.898 0.072 sVCAM-1 1.057 135.866 0.292 RANTES -6.234 122.791 0.000 EGFR -13.090 132.731 0.000 LRG1 9.952 119.311 0.000 D.Dimer 10.474 129.949 0.000

As a result, as shown in Table 3, ApoA1, ApoA2, CEA, CA19-9, TTR, CRP, VDBP, RANTES, EGFR, LRG1, D.Dimer, and PAI-1 markers showed differences in expression levels between gastric cancer patients and normal individuals. There was significance (Table 3).

Biomarker  Attribution

Constructing a classification model using ApoA1, ApoA2, CEA, CA19-9, TTR, CRP, VDBP, RANTES, EGFR, LRG1, D. Dimer or PAI-1 markers determined as significant markers in Example 3, The contribution of biomarkers in each classification model was measured.

Specifically, the classification model was constructed using a logistic regression model, which is represented by Equation 1 below.

[Equation 1]

In Equation 1, X ₁ to X _p are values obtained by standardizing the measured data of each biomarker determined as a significant marker in Example 3 with an average of 0 and a variance of 1, and β ₀ to β _p are unknown variables. Unknown variables can be analyzed using the R Statistical Package (R Development Core Team (2007), R: A language and environment for statistical computing.R Foundation for Statistical Computing, Austria, ISBN 3-900051-07-0, URL http://www.R-project.org). The greater the absolute value of the ridge estimate, the greater the contribution from the logistic regression model of the biomarker.

As a result, the contribution from the classification model decreased in the order of EGFR> TTR> ApoA1> RANTES> LRG1> D. Dimer> ApoA2> VDBP> PAI-1> CEA> CRP> CA19-9.

variety Biomarker  Measure performance of combinations

The contributions measured in Example 4 were used to remove markers with low contributions, and the performance of various biomarker combinations determined as significant markers in Example 3 was measured.

Specifically, samples of the normal and gastric cancer patients obtained in Example 1 were randomly divided, and 80% were the training data and 20% were the evaluation data. On the other hand, 12 combinations were prepared from 12 markers determined as significant markers in Example 3. Then, using the training group, a classification model was created in the same manner as in Example 4 for the combination, and the training group was predicted to the model. The above steps were repeated 1000 times to obtain 1000 AUC values, and the average of the values was calculated to determine how well the model predicted the training population to measure the performance of various biomarker combinations.

Performance of various biomarker combinations Marker combination Average (AUC) EGFR, TTR, ApoA1, RANTES, LRG1, D. Dimer, ApoA2, VDBP, PAI-1, CEA, CRP, CA19-9 0.96068 EGFR, TTR, ApoA1, RANTES, LRG1, D. Dimer, ApoA2, VDBP, PAI-1, CEA, CRP 0.96113 EGFR, TTR, ApoA1, RANTES, LRG1, D. Dimer, ApoA2, VDBP, PAI-1, CEA 0.96128 EGFR, TTR, ApoA1, RANTES, LRG1, D. Dimer, ApoA2, VDBP, PAI-1 0.96186 EGFR, TTR, ApoA1, RANTES, LRG1, D. Dimer, ApoA2, VDBP 0.96075 EGFR, TTR, ApoA1, RANTES, LRG1, D.Dimer, ApoA2 0.96184 EGFR, TTR, ApoA1, RANTES, LRG1, D.Dimer 0.96548 EGFR, TTR, ApoA1, RANTES, LRG1 0.96268 EGFR, TTR, ApoA1, RANTES 0.95506 EGFR, TTR, ApoA1 0.94155 EGFR, TTR 0.92569 EGFR 0.86507

As a result, as shown in Table 4, it was confirmed that the classification model made with the combination of EGFR, TTR, ApoA1, RANTES, LRG1 and D.Dimer biomarker had the largest AUC value, and finally EGFR, TTR as biomarker of gastric cancer. , ApoA1, RANTES, LRG1 and D. Dimer were selected (Table 4).

Performance Measurement of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer Biomarker Combinations

In Example 5, the performance of the biomarker was confirmed by measuring the specificity, sensitivity, and accuracy of the classification model made of the EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer biomarker combinations having the largest AUC. Specificity is a ratio of a normal person determined to be normal, calculated as shown in Equation 2 below, and sensitivity is a ratio determined to cancer patients as a cancer, calculated as shown in Equation 3 below. (accuracy) was calculated as the average of sensitivity and specificity at the ratio of normal to cancer.

[Equation 2]

[Equation 3]

Performance of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer Biomarker Combinations Specificity Sensitivity Accuracy Threshold 90.00% 91.06% 90.20% 0.1991

As a result, as shown in Table 5, classification models made with EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer biomarker combinations showed 90% specificity, 91.06% sensitivity, 90.20% accuracy, and 0.1991 threshold. The values are shown (Table 5). Through this, it was confirmed that if the threshold value is greater than 0.1991, it can be diagnosed as gastric cancer patient and less than 0.1991 as a normal person.

Validation of classification models made with EGFR, TTR, ApoA1, RANTES, LRG1 and D.Dimer biomarker combinations

The classification model made from the combination of EGFR, TTR, ApoA1, RANTES, LRG1 and D.Dimer biomarker generated in Example 5 was verified by the following method.

First, except that 100 normal patients from Seoul National University Hospital family medicine and 17 and 80 gastric cancer patients from Keimyung University Biobank and Pusan National University Human Resource Bank, respectively. Obtained. EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer proteins were quantified in the same manner as in <Example 2>, and then the specificity, sensitivity, accuracy, and threshold values were measured in the same manner as in <Example 6>. When the value is 0.1991, specificity, sensitivity, and accuracy were confirmed.

Performance of Classification Models Made from Combinations of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer Biomarkers Threshold Specificity Sensitivity Accuracy 0.1991 91.00% 91.75% 91.37%

As a result, as shown in Table 6, the classification model made from the combination of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer biomarker showed 91.00% specificity, 91.75% sensitivity and 91.37% sensitivity when the threshold value was 0.1991. It was confirmed that it represents the accuracy (Table 6). Through this, the classification model made with the combination of EGFR, TTR, ApoA1, RANTES, LRG1, and D.Dimer biomarker can distinguish gastric cancer patients from normal people with high accuracy.

Claims (10)

Epidermal growth factor receptor (EGFR), transthyretin (TTR), apolipoprotein A1 (ApoA1), regulated on activation, normal T cell expressed and secreted (RANTES), leucine-rich alpha-2-glycoprotein 1 (RGR1) and D.Dimer proteins Gastric cancer diagnostic composition comprising a detection reagent that specifically binds to.
According to claim 1, wherein the detection reagent is any one or more selected from the group consisting of antibodies, antibody fragments, aptamers and D-peptide, gastric cancer diagnostic composition.
The composition for diagnosing gastric cancer according to claim 2, wherein the antibody is a monoclonal antibody or a polyclonal antibody.
Gastric cancer diagnostic kit comprising the composition for diagnosing gastric cancer of claim 1.
Any one selected from the group consisting of primers, probes and anti-sense nucleotides that specifically bind to the nucleic acid sequences of genes encoding EGFR, TTR, ApoA1, RANTES, LRG1 and D.Dimer proteins Gastric cancer diagnostic kit containing the above.
1) measuring the expression level of EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer protein from the sample; And
2) comparing the protein expression level of step 1) with the protein expression level of a normal individual.
The method of claim 6, wherein the sample of step 1) is urine, blood, serum or plasma.
The method of claim 6, wherein the expression levels of the EGFR, TTR, ApoA1 and RANTES proteins are reduced compared to normal individuals.
The method of claim 6, wherein the expression levels of the LRG1 and D.Dimer proteins are increased compared to normal individuals.
1) measuring the expression level of the gene encoding the EGFR, TTR, ApoA1, RANTES, LRG1 and D. Dimer protein from the sample; And
2) comparing the gene expression level of step 1) with the gene expression amount of a normal individual.