WO2024085447A1 - Biomarqueur pour diagnostic de cancer du foie et méthode de fourniture d'informations basée sur l'intelligence artificielle pour diagnostic de cancer du foie - Google Patents

Biomarqueur pour diagnostic de cancer du foie et méthode de fourniture d'informations basée sur l'intelligence artificielle pour diagnostic de cancer du foie Download PDF

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WO2024085447A1
WO2024085447A1 PCT/KR2023/013630 KR2023013630W WO2024085447A1 WO 2024085447 A1 WO2024085447 A1 WO 2024085447A1 KR 2023013630 W KR2023013630 W KR 2023013630W WO 2024085447 A1 WO2024085447 A1 WO 2024085447A1
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liver cancer
biomarker
algorithm
cancer diagnosis
level
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Korean (ko)
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김승구
김기태
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(주)이노베이션바이오
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • the present invention relates to a biomarker for liver cancer diagnosis and an artificial intelligence-based information provision method for liver cancer diagnosis, and more specifically, histidine-rich glycoprotein (HRG), osteopontin (OPN), and complement components. It relates to a biomarker composition for liver cancer diagnosis consisting of C7 (complement component C7, C7) and alpha-fetoprotein (AFP) and an artificial intelligence-based information provision method for liver cancer diagnosis using the biomarker.
  • HRG histidine-rich glycoprotein
  • OPN osteopontin
  • complement components a biomarker composition for liver cancer diagnosis consisting of C7 (complement component C7, C7) and alpha-fetoprotein (AFP) and an artificial intelligence-based information provision method for liver cancer diagnosis using the biomarker.
  • Cancer is a disease in which cells proliferate infinitely and interfere with normal cell function. Representative examples include liver cancer, lung cancer, stomach cancer, breast cancer, colon cancer, and ovarian cancer, but can occur in virtually any tissue.
  • cancer diagnosis was based on external changes in biological tissue due to the growth of cancer cells, but recently, detection of trace amounts of biomolecules present in biological tissues or cells, such as blood, glyco chains, and DNA, has been used to diagnose cancer. Diagnosis is being attempted. However, the most commonly used cancer diagnosis method is diagnosis using tissue samples obtained through biopsy or imaging.
  • biopsy has the disadvantage of causing great pain to the patient, being expensive, and taking a long time to make a diagnosis.
  • the patient actually has cancer there is a risk that the cancer may metastasize during the biopsy process, and in areas where tissue samples cannot be obtained through biopsy, the suspected tissue may be removed through surgical operation.
  • the disadvantage is that it is impossible to diagnose the disease until it is extracted.
  • liver cancer hepatocellular carcinoma is the most common type of liver cancer in adults and the third cause of death due to cancer (Stefaniuk P, et al. , World J Gastroenterol. , 16:418 -424, 2010).
  • the most commonly used tumor marker in liver cancer is alpha-fetoprotein (AFP). Since AFP exists only at extremely low concentrations in adults (normally less than 7-10 ng/mL), the level of AFP in the blood increases. may indicate a serious increase in tumor cells.
  • liver cancer is diagnosed when AFP increases above 20 ng/mL, but diagnosis using AFP has the disadvantage of having a sensitivity as low as about 40%. In other words, since AFP-negative liver cancer accounts for more than 40% of all liver cancers, there is a need for additional markers that can more accurately and effectively increase the liver cancer diagnosis rate.
  • liver cancer markers that are helpful in diagnosing liver cancer, although not established as diagnostic criteria, include protein induced by vitamin K absence II, PIVKA-II or des-gamma-carboxy prothrombin. , DCP), Lectin-reactive alpha-fetoprotein (AFP-L3) fraction, alpha-L-fucosidase (AFU), glypican-3, Heat shock protein 70 (HSP 70), etc.
  • DCP Lectin-reactive alpha-fetoprotein
  • AFU alpha-L-fucosidase
  • HSP 70 Heat shock protein 70
  • the present inventors made diligent efforts to select markers that can more accurately diagnose liver cancer, and as a result, histidine-rich glycoprotein (HRG), osteopontin (OPN), and complement component C7 (complement component C7) were found. Biomarkers consisting of C7, C7), and alpha-fetoprotein (AFP) were selected, and it was confirmed that the expression levels of these biomarkers showed different patterns in the patient group and the normal control group. In addition, as a result of analyzing the quantitative values of these four biomarkers using an artificial intelligence-based algorithm, it was confirmed that the liver cancer diagnostic ability was improved, and the present invention was completed.
  • HRG histidine-rich glycoprotein
  • OPN osteopontin
  • C7 complement component C7
  • AFP alpha-fetoprotein
  • the purpose of the present invention is to provide a biomarker composition for diagnosing liver cancer.
  • Another object of the present invention is to provide a composition for diagnosing liver cancer containing a substance for measuring the expression level of the biomarker and a diagnostic kit for liver cancer using the same.
  • Another purpose of the present invention is to provide an artificial intelligence-based information provision method for liver cancer diagnosis using the biomarkers.
  • the present invention is a liver cancer treatment consisting of histidine-rich glycoprotein (HRG), osteopontin (OPN), complement component C7 (C7), and alpha-fetoprotein (AFP).
  • HRG histidine-rich glycoprotein
  • OPN osteopontin
  • C7 complement component C7
  • AFP alpha-fetoprotein
  • the biomarker can be extracted from blood.
  • the blood may be whole blood, plasma, or serum.
  • the present invention provides a composition for diagnosing liver cancer, including an agent for measuring the blood level of the biomarker composition for diagnosing liver cancer.
  • the agent for measuring the level of the biomarker composition is an agent for measuring the protein level, and the agent for measuring the protein level is specific to the protein or peptide fragment of the biomarker. It may be an antibody, interacting protein, ligand, nanoparticle, or aptamer that binds.
  • the present invention provides a kit for diagnosing liver cancer, including an agent for measuring the blood level of the biomarker composition for diagnosing liver cancer.
  • the present invention provides (a) histidine-rich glycoprotein (HRG), osteopontin (OPN), complement component C7 (C7), and alpha-fetoprotein of the test subject. , AFP) measuring the level of a biomarker for liver cancer diagnosis; and
  • the blood in step (a) may be whole blood, plasma, or serum.
  • the concentration of the biomarker in step (a) is determined by protein chip analysis, immunoassay, ligand binding assay, MALDI-TOF MS (matrix-assisted laser desorption/ionization - time of flight mass spectrometry analysis, SELDI-TOF MS (surface-enhanced laser desorption/ionization - time of flight mass spectrometry) analysis, radioimmunoassay, radioimmunodiffusion method, ouchterlony immunodiffusion immunodiffusion method, rocket immunoelectrophoresis, tissue immunostaining, complement fixation analysis, two-dimensional electrophoresis analysis, liquid chromatography-mass spectrometry, It can be measured using LC-MS), LC-MS/MS (liquid chromatography-mass spectrometry/mass spectrometry), western blot, and ELISA.
  • the level of the biomarker in the test subject's blood can be input into the algorithm model and the occurrence of liver cancer can be output as an output value.
  • step (b) is,
  • HSG Histidine-rich glycoprotein
  • OPN osteopontin
  • C7 complement component C7
  • alpha-fetoprotein alpha
  • the level of the biomarker can be learned through a machine learning algorithm to create a liver cancer incidence prediction model.
  • the artificial intelligence may be machine learning or deep learning, and more specifically, the algorithm of step (b) is k-nearest neighbor. algorithm; Logistic regression algorithm; Discriminant analysis algorithm; Partial least squares-discriminant analysis algorithm; Support vector machine algorithm; decision tree algorithm; decision tree ensemble algorithm; and a linear or non-linear classification algorithm including a neural network algorithm.
  • the kernel function when the algorithm is a support vector machine algorithm, can be expressed as Equation 1 below.
  • the present invention includes a measuring unit that measures the level of a biomarker for liver cancer diagnosis in the blood of a test subject; and a cancer diagnosis unit that determines whether liver cancer has developed by inputting the biomarker level into a learned artificial intelligence algorithm. It provides an artificial intelligence-based liver cancer diagnosis and prediction device.
  • liver cancer screening ability using the algorithm developed in the present invention has a sensitivity of 76.25% and a specificity of 95.83%, and it has been confirmed that it has a very high accuracy compared to existing liver cancer screening methods. Therefore, the present invention can be used for liver cancer diagnosis. information can be provided effectively.
  • the present invention relates to histidine-rich glycoprotein (HRG), osteopontin (OPN), complement component C7 (C7), and alpha-fetoprotein (AFP). ) relates to a biomarker composition for diagnosing liver cancer.
  • HRG histidine-rich glycoprotein
  • OPN osteopontin
  • C7 complement component C7
  • AFP alpha-fetoprotein
  • the biomarker is extracted from blood, and the blood may be whole blood, plasma, or serum.
  • diagnosis means confirming the presence or characteristics of a pathological condition. For the purposes of the present invention, diagnosis is to determine whether liver cancer has developed.
  • diagnostic biomarker used in the present invention refers to polypeptides, nucleic acids (e.g. mRNA, etc.), lipids, glycolipids, glycoproteins, and sugars that show a significant increase or significant decrease in liver cancer patient groups compared to normal control groups. It refers to organic biomolecules such as (monosaccharides, disaccharides, oligosaccharides, etc.), and is preferably the biomarker composition for diagnosing liver cancer.
  • blood was obtained from a normal control group and a liver cancer patient group, and the concentrations of HRG, OPN, C7, and AFP in the blood were measured. It was confirmed that the blood concentration of the biomarker of the present invention was significantly different between the quantitative value of the liver cancer patient group and that of the normal control group. Compared to the normal control group, the concentration of OPN, C7, and AFP increased in the blood of the liver cancer patient group. , HRG concentration was confirmed to decrease.
  • composition for diagnosing liver cancer Composition for diagnosing liver cancer
  • the present invention relates to a composition for diagnosing liver cancer, including an agent for measuring the blood level of the biomarker composition for diagnosing liver cancer of the present invention.
  • composition for diagnosing liver cancer according to the present invention applies mutatis mutandis to the ⁇ Biomarker composition for diagnosing liver cancer> described above.
  • the agent for measuring the level of the biomarker composition may be an agent for measuring protein levels.
  • the agent for measuring the protein level may be an antibody, interacting protein, ligand, nanoparticles, or aptamer that specifically binds to the protein or peptide fragment of the biomarker. there is.
  • antibody used in the present invention refers to a substance that specifically binds to an antigen and causes an antigen-antibody reaction.
  • antibody refers to an antibody that specifically binds to the biomarker of the present invention.
  • Antibodies of the present invention include polyclonal antibodies, monoclonal antibodies, and recombinant antibodies.
  • aptamer used in the present invention is a biopolymer that inhibits protein interaction through three-dimensional binding with a specific target protein in the form of single or double helix DNA or RNA. It has the characteristic of binding to various target molecules.
  • aptamers may be small nucleic acids 15 to 50 bases long that fold into defined secondary and tertiary structures, such as stem-loop structures.
  • the aptamer preferably binds to the target high- or low-expression protein with an equilibrium dissociation constant K D (equilibrium dissociation constant) less than 10 -6 , 10 -8 , 10 -10 , or 10 -12.
  • K D equilibrium dissociation constant
  • the present invention relates to a kit for diagnosing liver cancer, which includes an agent for measuring the blood level of the biomarker composition for diagnosing liver cancer of the present invention.
  • composition for diagnosing liver cancer according to the present invention applies mutatis mutandis to the ⁇ Biomarker composition for diagnosing liver cancer> described above.
  • the kit can be manufactured by conventional manufacturing methods known in the art.
  • the kit may include, for example, a lyophilized antibody, a buffer solution, a stabilizer, an inactive protein, etc.
  • the kit may further include a detectable label.
  • detectable label refers to an atom or molecule that allows specific detection of a molecule containing a label among molecules of the same type without the label.
  • the detectable label may be attached to an antibody, interacting protein, ligand, nanoparticle, or aptamer that specifically binds to the protein or fragment thereof.
  • the detectable label may include a radionuclide, a fluorophore, or an enzyme.
  • the kit can be used according to various immunoassay or immunostaining methods known in the art.
  • the immunoassay or immunostaining method includes radioimmunoassay, radioimmunoprecipitation, immunoprecipitation, enzyme-linked immunosorbent assay (ELISA), capture-ELISA, inhibition or competition assay, and sandwich assay. , flow cytometry, immunofluorescence staining, and immunoaffinity purification.
  • the kit may be an ELISA kit, a protein chip kit, a rapid kit, or a multiple reaction monitoring (MRM) kit.
  • MRM multiple reaction monitoring
  • the present invention provides (a) histidine-rich glycoprotein (HRG), osteopontin (OPN), complement component C7 (complement component C7, C7) from the blood of a test subject, and measuring the level of a biomarker for liver cancer diagnosis consisting of alpha-fetoprotein (AFP); and
  • the blood in step (a) may be whole blood, plasma, or serum.
  • the concentration of the biomarker in step (a) is determined by protein chip analysis, immunoassay, ligand binding assay, and matrix-assisted laser desorption/ionization (MALDI-TOF MS).
  • time of flight mass spectrometry analysis SELDI-TOF MS (surface-enhanced laser desorption/ionization - time of flight mass spectrometry) analysis, radioimmunoassay, radioimmunodiffusion method, ouchterlony immunodiffusion method, rocket Rocket immunoelectrophoresis, tissue immunostaining, complement fixation assay, two-dimensional electrophoresis analysis, liquid chromatography-mass spectrometry (LC-MS), It can be measured using LC-MS/MS (liquid chromatography-mass spectrometry/mass spectrometry), western blot, and ELISA.
  • LC-MS liquid chromatography-mass spectrometry/mass spectrometry
  • the level of the biomarker in the test subject's blood can be input into the algorithm model and the occurrence of liver cancer can be output as an output value.
  • step (b) the algorithm model of step (b) is,
  • HSG Histidine-rich glycoprotein
  • OPN osteopontin
  • C7 complement component C7
  • alpha-fetoprotein alpha
  • the level of the biomarker can be learned through a machine learning algorithm to create a liver cancer incidence prediction model.
  • the artificial intelligence may be machine learning or deep learning
  • the algorithm of step (b) includes a k-nearest neighbor algorithm; Logistic regression algorithm; Discriminant analysis algorithm; Partial least squares-discriminant analysis algorithm; Support vector machine algorithm; decision tree algorithm; decision tree ensemble algorithm; and a linear or non-linear classification algorithm including a neural network algorithm.
  • the kernel function when the algorithm is a support vector machine algorithm, the kernel function can be expressed as Equation 1 below.
  • a prediction model was developed using quantitative values for the four biomarkers of the present invention measured in the blood of liver cancer patients and normal controls, and the algorithm uses a radial basis function A support vector machine using as the kernel was used. As a result of confirming the ability to diagnose early liver cancer using the developed prediction model, it was found to have an accuracy of 76.25% sensitivity and 95.83% specificity.
  • the artificial intelligence-based liver cancer diagnosis method using the four types of biomarkers of the present invention has a very high accuracy compared to other existing diagnosis methods.
  • the present invention relates to histidine-rich glycoprotein (HRG), osteopontin (OPN), complement component C7 (C7), and A measuring unit that measures the level of a biomarker for liver cancer diagnosis consisting of alpha-fetoprotein (AFP); and
  • liver cancer diagnosis and prediction device including a cancer diagnosis unit that inputs the biomarker level into a learned artificial intelligence algorithm to determine whether liver cancer has occurred.
  • Example 1 Measurement of biomarker concentration in blood of liver cancer patient group and normal control group
  • liver cancer 96 normal control patients and 80 liver cancer patients were selected through Seoul National University Bundang Hospital and Ajou University Hospital and blood was collected.
  • C7 enzyme-linked immunosorbent assay was performed to measure the amount of C7 protein.
  • the C7 ELISA kit was YN1203 ELISA kit (INNOBATION BIO, Korea).
  • the separated plasma was diluted 200,000 times using the dilution solution in the ELISA kit.
  • standard materials for producing the C7 standard curve were produced according to the manufacturer's manual.
  • the prepared samples and standards were placed in a plate coated with C7 antibody, sealed, and incubated at room temperature for 2 hours.
  • HRG enzyme-linked immunosorbent assay was performed to measure the amount of HRG protein.
  • the HRG ELISA kit (INNOBATION BIO, Korea) was used.
  • the separated plasma was diluted 200,000 times using the dilution solution in the ELISA kit.
  • standard materials for producing the HRG standard curve were produced according to the manufacturer's manual.
  • the prepared samples and standards were placed on a plate coated with HRG antibody, sealed, and incubated at room temperature for 2 hours. After 2 hours of incubation, the contents of each plate well were removed and washed three times using a washing solution. After the washing was completed, detection antibodies were added to the plate and incubated at room temperature for 1 hour. After 1 hour of incubation, the contents of each plate well were removed and washed three times using a washing solution. After the washing was completed, the substrate was added to the plate and incubated at room temperature for 20 minutes.
  • osteopontin enzyme-linked immunosorbent assay was performed to measure the amount of osteopontin protein.
  • the Osteopontin Human ELISA kit (INNOBATION BIO, Korea) was used.
  • the separated plasma was diluted 10 times using the dilution solution in the ELISA kit. Before use, the ELISA kit was washed three times with a washing solution, and then standard materials for creating an osteopontin standard curve were prepared according to the manufacturer's manual. The prepared samples and standards were placed on a plate coated with osteopontin antibody, sealed, and incubated at room temperature for 2 hours.
  • AFP enzyme-linked immunosorbent assay was performed to measure the amount of AFP protein.
  • the AFP ELISA kit (INNOBATION BIO, Korea) was used.
  • the separated plasma was diluted 6-fold using the dilution solution in the ELISA kit.
  • standard materials for producing the AFP standard curve were produced according to the manufacturer's manual.
  • the prepared samples and standards were placed in a plate coated with AFP antibody, sealed, and incubated at room temperature for 2 hours. After 2 hours of incubation, the contents of each plate well were removed and washed three times using a washing solution. After the washing was completed, detection antibodies were added to the plate and incubated at room temperature for 1 hour. After 1 hour of incubation, the contents of each plate well were removed and washed three times using a washing solution. After the washing was completed, the substrate was added to the plate and incubated at room temperature for 20 minutes.
  • Biomarker levels for 80 liver cancer patients HRG ( ⁇ g/ml) OPN (ng/ml) C7 ( ⁇ g/ml) AFP (ng/ml) AJ_C01 50.88 40.26 262.63 3.4 AJ_C02 30.1 27.64 41.09 57.51 AJ_C03 56.71 16.34 54.73 55.28 AJ_C04 89.38 32.14 104.27 2.33 AJ_C05 35.04 28.98 92.66 3 AJ_C06 39.66 22 131.41 25.91 AJ_C07 60.27 42.67 58.64 60.44 AJ_C08 97.54 8.3 110.62 19.93 AJ_C09 51.82 16.17 67.96 1.85 AJ_C10 36.77 20.54 54.59 4.61 AJ_C11 42.93 25.68 62.29 4.88 AJ_C12 40.66 24.74 39.34 2.11 AJ_C13 24.63 19.14 68.
  • a prediction model capable of diagnosing liver cancer was developed by applying a support vector machine algorithm that uses a radial basis function as the kernel to the quantitative values of four types of biomarkers.
  • a liver cancer incidence prediction model was learned using the kernel function represented by Equation 1 below and tuning algorithm parameters.
  • Equation 1 determines the range of influence of one training sample
  • C another parameter of the support vector machine used independently of the kernel function, determines the extent to which training samples are allowed to be misclassified. do. Since the learning model is either underfitting or overfitting depending on the values of both parameters, the optimal parameters were selected through repeated cross-validation.
  • liver cancer Normal Predicted liver cancer 61 4 93.85%ppv Predicted Normal 19 92 82.88% npv 76.25% sensitivity 95.83% specificity 86.93% accuracy
  • the liver cancer screening ability using the algorithm developed in the present invention has a sensitivity of 76.25% and a specificity of 95.83%, and it has been confirmed that it has a very high accuracy compared to existing liver cancer screening methods. Therefore, the present invention can be used for liver cancer diagnosis. It can be useful in providing information about

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Abstract

La présente invention concerne un biomarqueur pour un diagnostic de cancer du foie et une méthode basée sur l'intelligence artificielle pour fournir des informations pour le diagnostic de cancer du foie. Plus particulièrement, la présente invention concerne une composition de biomarqueurs pour un diagnostic de cancer du foie, comprenant une glycoprotéine riche en histidine (HRG), l'ostéopontine (OPN), le composant C7 du complément (C7), et l'alpha-foetoprotéine (AFP), ainsi qu'une méthode basée sur l'intelligence artificielle permettant de fournir des informations pour un diagnostic de cancer du foie à l'aide du biomarqueur. Après avoir établi un modèle d'algorithme basé sur l'intelligence artificielle pour un diagnostic de cancer du foie en utilisant le biomarqueur de diagnostic de cancer du foie sélectionné dans la présente invention, il a été constaté que la sensibilité de la capacité de dépistage du cancer du foie était de 76,25 % et la spécificité de 95,83 %, ce qui confirme une précision beaucoup plus élevée par rapport aux méthodes existantes de dépistage de cancer du foie. Par conséquent, la présente invention peut fournir efficacement des informations pour le diagnostic de cancer du foie.
PCT/KR2023/013630 2022-10-19 2023-09-12 Biomarqueur pour diagnostic de cancer du foie et méthode de fourniture d'informations basée sur l'intelligence artificielle pour diagnostic de cancer du foie WO2024085447A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140235494A1 (en) * 2011-09-21 2014-08-21 The Universify of North Carolina at Chapel Hill Methods, Kits and Computer Program Products using Hepatocellular Carcinoma (HCC) Biomarkers
US20150095069A1 (en) * 2013-10-01 2015-04-02 The Regents Of The University Of Michigan Algorithms to Identify Patients with Hepatocellular Carcinoma
KR20180123978A (ko) * 2017-05-10 2018-11-20 서울대학교산학협력단 간암 고위험군의 간암 발병 모니터링 또는 진단용 바이오마커 및 그 용도
US20210208147A1 (en) * 2017-10-16 2021-07-08 Biopredictive Method of prognosis and follow up of primary liver cancer
KR102421471B1 (ko) * 2021-10-06 2022-07-18 (주)이노베이션바이오 암 진단용 바이오마커 및 이의 용도

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140235494A1 (en) * 2011-09-21 2014-08-21 The Universify of North Carolina at Chapel Hill Methods, Kits and Computer Program Products using Hepatocellular Carcinoma (HCC) Biomarkers
US20150095069A1 (en) * 2013-10-01 2015-04-02 The Regents Of The University Of Michigan Algorithms to Identify Patients with Hepatocellular Carcinoma
KR20180123978A (ko) * 2017-05-10 2018-11-20 서울대학교산학협력단 간암 고위험군의 간암 발병 모니터링 또는 진단용 바이오마커 및 그 용도
US20210208147A1 (en) * 2017-10-16 2021-07-08 Biopredictive Method of prognosis and follow up of primary liver cancer
KR102421471B1 (ko) * 2021-10-06 2022-07-18 (주)이노베이션바이오 암 진단용 바이오마커 및 이의 용도

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