WO2016093567A1 - Biomarker for diagnosis of hepatoma and use thereof - Google Patents

Abstract

Disclosed in the present application is a marker or a maker combination enabling diagnosis, prognosis, or treatment monitoring of hepatoma with high accuracy and sensitivity. The marker according to the present application enables simple and effective diagnosis or monitoring of hepatoma by a noninvasive test using blood, thus can be developed into diverse test kits, and can be very usefully utilized in early detection of hepatoma at home or in general practice, for example, through POCT development.

Description

Biomarker for Diagnosing Liver Cancer and Uses thereof

The present invention relates to a biomarker for diagnosing liver cancer and its use.

Liver cancer or hepatocellular carcinoma (HCC) is the most common type of adult liver cancer, accounting for the third leading cause of death from cancer (Stefaniuk P, et al., 2010, World J Gastroenterol 16: 418-424). Liver cancer is a disease that appears only when the symptoms are quite advanced, which often leads to a missed time for proper treatment, and the prognosis is extremely poor when treated. In particular, surgical resection is a serious disease that dies within a year. Considering this clinical reality, early diagnosis and prognosis of cancer are the most realistic alternatives to cancer treatment and guidelines for next-generation liver cancer care. .

In particular, hepatocellular carcinoma is known to occur in patients with risk factors such as hepatitis virus, alcohol, and cirrhosis of the liver, and it is clear to be subjected to selective surveillance of high risk groups using early diagnosis markers. Indeed, early diagnosis at six-month intervals has been shown to increase survival (reduced by 37%) in patients with liver cancer.

Biomarker tests have not been developed to detect liver cancer early and correctly in normal people. Serum alpha-fetoprotein testing is used to diagnose non-invasive early liver cancer in high-risk patients. At the time of development, AFP suggested a baseline of 20 ng / mL to achieve both good sensitivity and specificity, but in this case the sensitivity is only 60% and is currently 200 ng / mL according to the International Society's Guidelines for Diagnosing Liver Cancer. Based on the diagnosis of liver cancer, the specificity increases, but the sensitivity is only 22%. Previous studies have shown that AFP has an overall sensitivity of about 66% and a specificity of 82%, limiting the diagnosis of all liver cancer patients.

Serum markers for diagnosis of liver cancer, although not established by other diagnostic criteria, include Descarboxyprothrombin (DCP), Prothrombin Induced by Vitamin K Absence II (PIVKA-II), glycosylated AFP to total AFP (L3 fraction) distribution, alpha fucosidase, glypican 3 and HSP-70. However, each of them has a meaning as a prognostic factor, and when used alone, its accuracy is low and it is not yet used as a screening test. Early diagnosis of liver cancer with a single serologic marker seems to have reached its limit. In addition, only 30% of patients with liver cancer are diagnosed at the stage of curable treatment such as actual surgery or radiofrequency ablation.

US Patent Publication No. 2010/0304372 relates to a method and composition for diagnosing liver cancer, which discloses a method for diagnosing liver cancer by detecting CpG islands in the BASF1, SPINT2, APC, CCND2, CFTR, RASSF1 and SRD5A2 genes.

Korean Patent No. 1141789 relates to a novel biomarker for liver cancer and its use, and discloses a marker for diagnosing or prognostic liver cancer through the detection of NK4 protein and the like.

Therefore, it is urgent to develop markers that can overcome the limitations of individual tests with improved specificity and sensitivity to allow early diagnosis of hepatic cancer.

The present invention is to provide a biomarker for diagnosing liver cancer.

In one embodiment the present application is directed to ALS (Insulin-like growth factor-binding protein complex acid labile subunit); AMBP (Alpha-1-microglobulin / bikunin precursor); CPB2 (Carboxypeptidase B2); CHLE (Cholinesterase); CLUS (Clusterin); CNDP1 (Beta-Ala-His dipeptidase); CO6 (Complement component C6); CPN2 (Carboxypeptidase N subunit 2); FA11 (Coagulation factor XI); FINC (Fibronectin); Hepatocyte growth factor activator (HGFA); Insulin-like growth factor-binding protein 3 (IBP3); Insulin-like growth factor II (IGF2); ITIH1 (inter-alpha-trypsin inhibitor heavy chain H1); KAIN (Kallistatin); KLKB1 (Plasma kallikrein); Phosphatidylcholine-sterol acyltransferase (LCAT); PLGA (Plasminogen-related protein A); PLMN (Plasminogen); THRB (Prothrombin); VTDB (Vitamin D binding protein); And it provides a composition for diagnosing or measuring liver cancer, comprising a reagent for detecting one or more biomarkers selected from the group consisting of VTNC (Vitronectin).

In another aspect, the present disclosure provides a method for the diagnosis or prognosis of liver cancer, or to provide information required therein, for example, to include an insulin-like growth factor-binding protein complex acid labile subunit (ALS); AMBP (Alpha-1-microglobulin / bikunin precursor); CPB2 (Carboxypeptidase B2); CHLE (Cholinesterase); CLUS (Clusterin); CNDP1 (Beta-Ala-His dipeptidase); CO6 (Complement component C6); CPN2 (Carboxypeptidase N subunit 2); FA11 (Coagulation factor XI); FINC (Fibronectin); Hepatocyte growth factor activator (HGFA); Insulin-like growth factor-binding protein 3 (IBP3); Insulin-like growth factor II (IGF2); ITIH1 (inter-alpha-trypsin inhibitor heavy chain H1); KAIN (Kallistatin); KLKB1 (Plasma kallikrein); Phosphatidylcholine-sterol acyltransferase (LCAT); PLGA (Plasminogen-related protein A); PLMN (Plasminogen); THRB (Prothrombin); VTDB (Vitamin D binding protein); And detecting the presence and / or concentration of nucleic acids and / or proteins of at least one biomarker selected from the group consisting of VTNC (Vitronectin); Comparing the detection result for the level or presence of the nucleic acid or protein with the corresponding result of the corresponding marker in the control sample; And comparing the control sample with a change in nucleic acid or protein concentration in the subject sample, or when there is a change in the presence or absence of the nucleic acid or protein, and determining it as liver cancer. It provides a method for detecting.

The marker according to the present invention can be detected using a blood sample, and is differentially expressed in liver cancer patients compared to samples derived from patients who have been cured after being treated with normal people, hepatitis, cirrhosis or liver cancer. It can be useful for early diagnosis of liver cancer and for measuring progress after treatment.

Biomarkers according to the present application can diagnose liver cancer with high specificity and sensitivity. In particular, liver cancer patients, as well as liver cirrhosis and liver cancer patients can be distinguished, patients can be diagnosed in advance from liver cirrhosis patients to liver cancer patients in advance, and liver cancer is cured, liver cirrhosis patients with liver cancer removed can be distinguished, liver cancer It can be used to predict treatment prognosis.

In addition, the marker according to the present application is very useful for early detection in homes and general clinics to enable non-invasive diagnosis using blood, and liver cancer can be detected through simple blood tests during health check-ups. It can bring savings.

The biomarker according to the present invention can be developed in the form of a diagnostic kit using an antibody, protein or peptide thereof, for example, ELISA, dipstick in dipstick form, MRM kit based on peptide. In particular, it can be industrialized by screening peptides by MRM instead of immunoassay methods in the existing clinical field of clinical practice. Furthermore, weighted analysis with AFP, known as liver cancer marker, improves and surpasses existing liver cancer diagnosis method. It can be used as.

Figure 1 schematically shows the MRM analysis process used for biomarker discovery and analysis.

Figure 2 shows the coelution (coelution) of the endogenous peptide and the synthetic peptide in the blood.

Figure 3 shows the Q3 intensity pattern of endogenous peptides and synthetic peptides in the blood.

Figure 4 shows a method for determining the endogenous peptide level of blood.

Figure 5 shows the results of the measurement of the endogenous peptide concentration of blood in the dynamic range (dynamic range) distribution.

6 shows the levels in blood for low and high abundance targets.

Figure 7 shows a schematic diagram of the SIS peptide injection concentration determination method.

8 shows the results of constructing a multiprotein marker panel between LC groups versus HCC groups.

9 shows the results of constructing a multiprotein marker panel between the HCC group versus the recovery group.

FIG. 10 shows Western blotting results of 10 target proteins with matching expression trends in order to verify early diagnosis markers. FIG. 10A shows protein-01, A2AP (alpha-2 -antiplasmin), and FIG. 10B shows a protein. -02, AMBP, FIG. 10C is Protein-03, AFP (alpha-fetoprotein), FIG. 10D is Protein-04, CATB (cathepsin B), FIG. 10E is Protein-05, FETUA (alpha-2- HS-glycoprotein), Figure 10f shows protein-06, FINC (fibronectin), Figure 10g shows protein-07, ITIH1 (Inter-alpha-trypsin inhibitor heavy chain H1), Figure 10h shows protein-08, ITIH3 (Inter-alpha-trypsin inhibitor heavy chain H3 10I shows protein-09, PLGA (Plasminogen-related protein A), and FIG. 10J shows protein-10. THRB (Prothrombin).

The present invention sequentially screens and verifies differentially expressed protein / peptide expression levels in liver cirrhosis, pre- and post-treatment samples using MRM technology, and shows significant differences between groups, which is useful as a biomarker for diagnosing liver cancer. Based on protein / peptide discovery.

Thus, in one embodiment, the present disclosure provides IGFALS (Insulin-like growth factor-binding protein complex acid labile subunit); AMBP (Alpha-1-microglobulin / bikunin precursor); CPB2 (Carboxypeptidase B2); CHLE (Cholinesterase); CLUS (Clusterin); CNDP1 (Beta-Ala-His dipeptidase); CO6 (Complement component C6); CPN2 (Carboxypeptidase N subunit 2); FA11 (Coagulation factor XI); FINC (Fibronectin); Hepatocyte growth factor activator (HGFA); Insulin-like growth factor-binding protein 3 (IBP3); Insulin-like growth factor II (IGF2); ITIH1 (inter-alpha-trypsin inhibitor heavy chain H1); KAIN (Kallistatin); KLKB1 (Plasma kallikrein); Phosphatidylcholine-sterol acyltransferase (LCAT); PLGA (Plasminogen-related protein A); PLMN (Plasminogen); THRB (Prothrombin); VTDB (Vitamin D binding protein); And a reagent for detecting one or more biomarkers selected from the group consisting of VTNC (Vitronectin), and a composition or kit for liver cancer diagnosis or prognosis measurement.

Liver cancer or hepatocellular carcinoma (HCC) herein refers to primary malignant tumors that occur in the liver tissue itself that occurs in patients with risk factors such as alcohol abuse, viral hepatitis and metabolic liver disease. Hepatic cancer has no fibrous stromal bleeding and cell necrosis, vascular infiltration into the portal system, and in severe cases can lead to hepatic rupture and intraperitoneal blood effusion. These liver cancers are chronic inflammations that occur in the liver, and are distinguished from cirrhosis or cirrhosis, which leads to regenerative nodule and fibrosis, resulting in hepatocellular damage, scars, decreased liver function, ascites, febrile disease, hepatic coma. Patients with liver cancer usually have both inflammation and fibrosis. Hepatitis is eliminated in most liver cancer patients by drugs.However, 90% of liver cirrhosis patients are diagnosed as liver cancer. It is important to do.

As used herein, the term diagnosis refers to determining the susceptibility to a subject's disease for a particular disease or condition, determining whether or not a person currently has a particular disease or condition, or transition of a subject having a particular disease or condition. Identifying a sex cancer state, determining the stage or progress of the cancer or determining the cancer's responsiveness to treatment or monitoring the state of the subject to provide information about the therapeutic efficacy (e.g., therapeutic efficacy) ).

Prognostic measurement herein includes treating and recovering after diagnosis of liver cancer.

As used herein, the term diagnostic biomarker or diagnostic marker (diagnosis marker) is a substance that can be diagnosed by distinguishing patients with liver cancer from patients with liver cirrhosis, normal cells or appropriate liver cancer treatment, derived from patients with liver cancer compared to control samples A sample of the protein, particularly a protein exhibiting a change in concentration in the blood, a polypeptide derived from the protein, a gene encoding the protein or a fragment thereof.

The biomarker according to the present invention has an increased amount in the blood of liver cancer patients compared to the sample of the control group, the sequence of which can be searched in UniProt DB (www.uniprot.org) for example with the ID described in Table 1.

The markers according to the invention can be used in one or two or more combinations, for example two, three, four, five combinations, existing markers such as AFP, and / or diagnostic methods such as liver ultrasound And the like can be used. Those skilled in the art will be able to select combinations of markers that meet the desired sensitivity and specificity through methods such as assays and / or logistic regression assays using biological samples from subjects, including normal subjects and patients, such as those described herein. will be.

In one embodiment according to the invention in particular one or more markers of ALS, CBPB2, CLUS, CNDP1, CPN2, FA11, FINC, or HGFA are used, the markers being in both primary and secondary sample groups as described in the Examples herein. High AUC values showed differential expression in control and liver cancer patients.

In another embodiment according to the invention FINC markers alone, or THRB and FINC; Or ALS, ITIH1, THRB and FINC combinations.

In another embodiment according to the present invention the biomarker for determining recovery after liver cancer is selected from the group consisting of THRB, FINC, ITIH1, IBP3, PLMN, CBPB2 and PLGA, wherein the marker or combination of markers is particularly high AUC value , But does not exclude the combination of other markers.

In other embodiments one or more markers according to the present disclosure can be used in combination with an AFP marker.

The markers according to the invention can be detected at the level of the detection of the presence of nucleic acids, in particular of proteins and / or mRNA, and / or the amount of expression thereof, changes in the amount of expression, difference in the amount of expression through quantitative or qualitative analysis.

Detection herein includes quantitative and / or qualitative analysis, including the detection of presence, absence, and expression level detection. Such methods are well known in the art and those skilled in the art will select appropriate methods for carrying out the present application. Could be.

Detection of such markers according to the present application may be based on functional and / or antigenic characteristics of the marker.

In one embodiment the marker according to the present application can be detected using the detection of the activity or function of the marker, or using a nucleic acid encoding a protein, in particular an agent that specifically interacts at the mRNA level and / or protein level.

In another embodiment the detection of a marker according to the present application may be carried out through the detection of the corresponding peptide from the marker protein, for example the corresponding peptide for each marker described in Tables 1-1 and 2. One or more peptides may be used for one protein.

In this respect, the detection reagent included in the composition according to the present invention is a reagent capable of detecting the marker according to the present invention through quantitative or qualitative analysis in various ways at the protein or nucleic acid level.

For quantitative and qualitative analysis of the markers according to the present application, various methods for qualitatively or quantitatively detecting known proteins or nucleic acids can be used.

Qualitative or quantitative detection methods at the protein level include, for example, Western blot, ELISA, radioimmunoassay, immunodiffusion, immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, antibody labeled in solution / suspension The method may be used by binding with, a mass spectrometer or a protein array using an antibody, or the like.

Alternatively, as a qualitative or quantitative detection method at the nucleic acid level, a method using a nucleic acid transcription and amplification method, an eTag system, a system based on labeled beads, an array system such as a nucleic acid array, and the like can be used.

Such methods are known and are described, for example, in chip-based capillary electrophoresis: Colyer et al. 1997. J Chromatogr A. 781 (1-2): 271-6; mass spectroscopy: Petricoin et al. 2002. Lancet 359: 572-77; eTag systems: Chan-Hui et al. 2004. Clinical Immunology 111: 162-174; microparticle-enhanced nephelometric immunoassay: Montagne et al. 1992. Eur J Clin Chem Clin Biochem. 30: 217-22, and the like.

In one embodiment according to the present disclosure, the marker can be detected using mass spectrometry, which can be analyzed for example in the manner described in the Examples herein after separating the protein or peptide from the sample. See, eg, Kim, et al. 2010 J Proteome Res. 9: 689-99; Anderson, L et al. 2006. Mol Cell Proteomics 5: 573-88. In one embodiment, multiple reaction monitoring (MRM) techniques using, for example, Triple Quadrupole LC-MS / MS, QTRAP, and the like are used. MRM is a method that can quantitatively and multi-quantitatively measure multiple substances such as traces of biomarkers present in a biological sample, using a first mass filter (Q1). Optionally pass to the crash tube. Proton ions that arrive at the impingement tube then collide with the internal impingement gas, split and form product ions or daughter ions to be sent to the second mass filter Q2, where only the characteristic ions are transferred to the detector. In this way, it is a high selectivity and sensitivity analysis method that can detect only the information of the desired component. See, for example, those described in Gillette et al., 2013, Nature Methods 10: 28-34.

In another embodiment, a binding agent or array comprising binding agents that specifically binds to each protein or mRNA from a gene encoding the protein is used.

In another embodiment, a sandwich-type immunoassay such as Enzyme Linked Immuno Sorbent Assay (ELISA) or Radio Immuno Assay (RIA) may be used. This method involves a biological sample on a first antibody bound to a solid substrate such as beads, membranes, slides or microtiterplates made of glass, plastic (eg polystyrene), polysaccharides, nylon or nitrocellulose. After the addition of a label, a label capable of direct or indirect detection may be labeled with a radioactive substance such as ³ H or ¹²⁵ I, a fluorescent substance, a chemiluminescent substance, hapten, biotin, digoxygenin, or the like, or the action of a substrate. Proteins can be detected qualitatively or quantitatively through binding of conjugated antibodies with enzymes such as horseradish peroxidase, alkaline phosphatase, and malate dehydrogenase, which are capable of developing or emitting light.

In other embodiments, immunoelectrophoresis such as Ouchterlony plates, Western blots, Crossed IE, Rocket IE, Fused Rocket IE, Affinity IE, which can simply detect markers through antigen antibody binding, can be used. The immunoassay or method of immunostaining is described in Enzyme Immunoassay, E. T. Maggio, ed., CRC Press, Boca Raton, Florida, 1980; Gaastra, W., Enzyme-linked immunosorbent assay (ELISA), in Methods in Molecular Biology, Vol. 1, Walker, J.M. ed., Humana Press, NJ, 1984 and the like. By analyzing the final signal intensity by the above-described immunoassay process, that is, by performing a signal contrast with a normal sample, it is possible to diagnose whether the disease occurs.

Reagents or materials used in such methods are known and include, for example, antibodies, substrates, nucleic acids or peptide aptamers that specifically bind to the marker, or receptors or ligands or cofactors that specifically interact with the marker. And the like can be used. Reagents or materials that specifically interact with or bind to the markers of the present disclosure may be used with chip or nanoparticles.

Markers herein can also be detected quantitatively and / or qualitatively using a variety of methods known at the nucleic acid level, particularly at the mRNA level.

Qualitative or quantitative detection methods at the nucleic acid level include, for example, reverse transcriptase polymerase chain reaction (RT-PCR) / polymerase chain reaction, competitive RT-PCR, for detection of mRNA levels, expression levels or patterns. Real-Time RT-PCR, Nuclease Protection Assays (NPA) For example, RNase, S1 nuclease assays, in situ hybridization, DNA microarrays or chips or Northern blots can be used, such assays are known and also known. It may be carried out using commercially available kits and one skilled in the art will be able to select the appropriate one for the practice herein. For example, Northern blots can be used to determine the size of transcripts present in a cell, have the advantage of using a variety of probes, NPAs are useful for multiple marker analysis, and in situ hybridization can be used for cells of transcripts such as mRNA. Or, it is easy to locate in the tissue, and reverse transcription polymerase chain reaction is useful for detecting a small amount of sample. In addition, an array including a binding agent or a binding agent that specifically binds to a nucleic acid such as mRNA or cRNA from a gene encoding a biomarker protein according to the present application can be used.

Reagents or substances used in the method for detecting the biomarker at the nucleic acid level are known, for example, as a detection reagent in a method for measuring the presence and amount of mRNA by RT-PCR, for example, a polymerase. , Probes and / or primer pairs specific for the mRNA of the marker herein. "Primer" or "probe" refers to a nucleic acid sequence having a free 3 'hydroxyl group capable of complementarily binding to a template and allowing reverse transcriptase or DNA polymerase to initiate replication of the template. do. As used herein, the detection reagent may be labeled with a colorant, luminescent or fluorescent substance as described above for signal detection. In one embodiment, Northern blot or reverse transcription PCR (polymerase chain reaction) is used for mRNA detection. In the latter case, the RNA of a sample is specifically isolated from mRNA, and then cDNA is synthesized therefrom, and then a specific gene or a combination of primers and probes is used to detect a specific gene in the sample. Or it is a method which can determine the expression amount. Such methods are described, for example, in Han, H. et al, 2002. Cancer Res. 62: 2890-6.

The detection reagent included in the composition according to the present application may be labeled directly or indirectly in a sandwich form for detection depending on the specific method used for detection. In the case of the direct labeling method, serum samples used for arrays and the like are labeled with fluorescent labels such as Cy3 and Cy5. In the case of a sandwich, an unlabeled serum sample is first detected by reacting with an array to which a detection reagent is attached, followed by binding to a target protein with a labeled detection antibody. In the case of the sandwich method, the sensitivity and specificity can be increased, and thus the detection can be performed up to pg / mL level. In addition, radioactive materials, coloring materials, magnetic particles and high-density electron particles may be used as the labeling material. Fluorescence luminosity can be used with scanning confocal microscopy, for example Affymetrix, Inc. Or Agilent Technologies, Inc.

The compositions herein may further comprise one or more additional ingredients required for binding assays and may further include, for example, binding buffers, reagents for sample preparation, blood sampling syringes or negative and / or positive controls. .

The composition of the present invention comprising various detection reagents as described above is for ELISA analysis, dip stick rapid kit analysis, MRM analysis kit, microarray, gene amplification, or immunity depending on the assay. It may be provided for analysis and the like, and an appropriate detection reagent may be selected according to the analysis mode.

In one embodiment an ELISA or dipstick rapid kit is used, wherein an antibody that recognizes one or more markers according to the present application is attached to a substrate, such as the surface of a well or glass slide of a multiwell plate or nitrocellulose. Can be. In the case of dipsticks, a technique widely used in the field of point of care test (POCT), in which one or more antibodies recognizing a biomarker according to the present invention are bound to a substrate such as nitrocellulose, and contacted with a sample such as serum. For example, when one end of a dipstick is attached to a serum sample, the marker is detected in such a manner that the sample moves through the substrate by capillary action and develops color upon binding to the antibody in the substrate.

In another embodiment, an MRM kit based on a peptide is provided, and the MRM scheme is as described above. The MRM method uses a peptide that selectively recognizes a specific protein, and can more stably detect a marker in a biological sample as compared with a conventional method using an antibody sensitive to the environment such as temperature and humidity. For example, the peptides described in Tables 1-1 and 1-2 herein may be used, and one or more peptides may be used in one marker. For example, peptides corresponding to proteins (expressed as short amino acids) include Prothrombin (THRB) -HQDFNSAVQLVENFCR, SGIECQLWR; Fibronectin (FINC) -WCGTTQNYDADQK, GEWTCIAYSQLR, HTSVQTTSSGSGPFTDVR; ITIH1-EVAFDLEIPK, LDAQASFLPK; IBP3-ALAQCAPPPAVCAELVR; PLMN-LSSPAVITDK, EAQLPVIENK; CBPB2-DTGTYGFLLPER, YPLYVLK; Same as PLGA-DVVLFEK.

In other embodiments, it may be provided in the form of an array or chip comprising a microarray. Detection reagents may be attached to the surface of a substrate such as glass or nitrocellulose, and array fabrication techniques are described, for example, in Schena et al., 1996, Proc Natl Acad Sci USA. 93 (20): 10614-9; Schena et al., 1995, Science 270 (5235): 467-70; And U.S. Pat. Nos. 5,599,695, 5,556,752 or 5,631,734. Detection reagents that can be attached to an array include, for example, antibodies, antibody fragments, aptamers, aviders, or peptidomimetics capable of specific binding to a protein.

In another aspect the invention relates to a kit or system for diagnosing or prognosticing liver cancer comprising a reagent for detecting a biomarker. Detection reagents and methods in which such reagents are used are described above. Reagents capable of detecting such markers of the present application may be separately dispensed in a compartment in which the compartment is divided, and in this sense, the present application also relates to an apparatus / apparatus comprising compartmentally containing the marker detection reagent of the present application. The kit may also include additional instructions for use.

In another aspect, the present invention provides a method for the diagnosis or prognosis of liver cancer, comprising: Insulin-like growth factor-binding protein complex acid labile subunit (ALS) from a biological sample from a subject; AMBP (Alpha-1-microglobulin / bikunin precursor); CPB2 (Carboxypeptidase B2); CHLE (Cholinesterase); CLUS (Clusterin); CNDP1 (Beta-Ala-His dipeptidase); CO6 (Complement component C6); CPN2 (Carboxypeptidase N subunit 2); FA11 (Coagulation factor XI); FINC (Fibronectin); Hepatocyte growth factor activator (HGFA); Insulin-like growth factor-binding protein 3 (IBP3); Insulin-like growth factor II (IGF2); ITIH1 (inter-alpha-trypsin inhibitor heavy chain H1); KAIN (Kallistatin); KLKB1 (Plasma kallikrein); Phosphatidylcholine-sterol acyltransferase (LCAT); PLGA (Plasminogen-related protein A); PLMN (Plasminogen); THRB (Prothrombin); VTDB (Vitamin D binding protein); And detecting the presence and / or concentration of nucleic acids and / or proteins of at least one biomarker selected from the group consisting of VTNC (Vitronectin).

Such methods further comprise comparing the detection results for the level or presence of the nucleic acid or protein with the corresponding results of the corresponding markers in the control sample; And comparing the control sample with a change in nucleic acid or protein concentration of the subject sample, or when there is a change in the presence or absence of the nucleic acid or protein, and determining it as liver cancer.

Samples that can be used as a control for the diagnosis of liver cancer patients in the method according to the present application may be a sample from a normal person, hepatitis, liver cirrhosis or patients who have been cured after treatment (liver cirrhosis). Compared with this control patient, the expression level of the marker according to the present application is increased in liver cancer patients.

The biological sample used in the method according to the present invention may be whole blood, serum or plasma.

The marker according to the present application increases the expression level in liver cancer patients as well as samples from normal people, as well as liver cirrhosis patients.

Therefore, the liver cancer diagnosis in the method according to the present application can distinguish between liver cirrhosis and liver cancer patients, it is possible to diagnose patients from liver cirrhosis to liver cancer is particularly advantageous for early diagnosis. In this case, a sample derived from a liver cirrhosis patient may be included as a control, and the sample derived from a liver cirrhosis patient includes a liver cancer cure patient who maintains cirrhosis. It is also advantageous for the identification of patients recovered after liver cancer treatment. In this case, liver cancer samples from the same patient can be used as a control.

As used herein, biological samples refer to substances or mixtures of substances that include one or more components capable of detecting a biomarker and include, but are not limited to, organisms, particularly body fluids, in particular whole blood, plasma, serum or urine.

In one embodiment according to the invention in particular one or more markers of ALS, CBPB2, CLUS, CNDP1, CPN2, FA11, FINC, or HGFA are used, the markers being in both primary and secondary sample groups as described in the Examples herein. High AUC values showed differential expression in control and liver cancer patients.

In another embodiment according to the present invention the biomarker for determining whether to recover after treatment may be selected from the group consisting of THRB, FINC, ITIH1, IBP3, PLMN, CBPB2 and PLGA.

In another embodiment according to the invention FINC markers alone, or THRB and FINC; Or ALS, ITIH1, THRB and FINC combinations.

In other embodiments one or more markers according to the present disclosure can be used in combination with an AFP marker.

The control or reference group used in the present method, the biomarker detection method, and the reagents and the data analysis method for the determination may be referred to the above and the following.

The method according to the invention can be carried out using the above-mentioned method or a method using a detection reagent, and in particular can be carried out by protein or nucleic acid microarray analysis, nucleic acid amplification, antigen-antibody reaction, or mass spectrometry. .

The methods herein include mammals, in particular humans. Human subjects include those suspected of developing HCC, patients with hepatitis, cirrhosis of the liver, or persons who are not suspected of having HCC diagnosed. In one embodiment, the method is directed to determining whether a subject has HCC symptoms, such as abdominal pain, hypertrophy, ascites, jaundice, muscle weakness, hepatitis (eg, HCV infection), or esophageal varices. It may be performed on a subject with other symptoms. In another embodiment, the subject is apparently normal in the absence of HCC symptoms.

In other embodiments, the subject is a person who may have low or normal plasma AFP concentrations and may not be diagnosed with HCC based on AFP. In this case, the serum concentration of AFP is about 0 μg / l (non-detected) to 20 μg / l, eg, 0 μg / l (non-detected) to 5 μg / l, 5 μg / l to 10 μg / l, 10 μg / l to 15 μg / l Or 15 μg / l to 20 μg / l.

When using a combination of two or more markers using the methods described above, a dataset may be generated that includes a profile, ie, quantitative information related to marker protein expression in a sample.

After obtaining a profile using a marker, a comparison of the result with a reference group or a control group determines whether a sample of the subject has hepatocellular carcinoma. As a control or reference group, a negative control group, or a sample from a patient treated after hepatocellular carcinoma, a positive control group, a sample from a patient determined as hepatocellular carcinoma by a method other than the marker according to the present application, a sample from a cirrhosis patient , A sample of a patient derived from hepatitis.

In one embodiment according to the present application, a sample derived from a normal person, a sample derived from a patient treated after being determined as a hepatocellular carcinoma, is used as a control or a reference group, and used for comparison of the obtained profile.

Known methods can be used to compare marker profiles between control groups and test groups using samples. For example, digital image comparison of expression profiles, comparisons using DB for expression data, or U.S. See patents 6,308,170 and 6,228,575.

Profiles obtained through marker detection according to the present application can be processed using known data analysis methods. Examples include nearest neighbor classifiers, partial-least squares, SVM, AdaBoost, and clustering-based classification methods, for example Ben-Dor et al (2007, J. Comput. Biol. 7: 559-83), Nguyen et al (2002, Bioinformatics 18: 39-50), Wang et al (2003, BMC Bioinformatics 4:60), Liu et al (2001, Genome Inform. Ser. Workshop Genome Inform. 12: 14-23), Yeang et al (2001, Bioinformatics 17 Suppl 1: S316-22) and Xiong (2000, Biotechniques 29 (6): 1264-8, 1270) and the like.

In addition, various statistical processing methods may be used to determine that the results detected through the markers of the present application are significant for hepatocellular carcinoma discrimination. As a statistical processing method, a logic regression method is used in one embodiment, and may be referred to Ruczinski, 2003, Journal of Computational and Graphical Statistics 12: 475-512. The method is similar to the CART method in which a classifier is presented as a binary tree, but each node uses a more general Boolean operator associated with the property compared to the “and” operator generated by CART. Examples of other analysis methods include nearest shrunken centroids (Tibshirani. 2002 PNAS. 99: 6567-72), random forests (Breiman. 2001. Machine Learning 45: 5-32, and MART (Hastie. 2001. The Elements of Statistical Learning, Springer). ).

In one embodiment, statistical processing can determine the level of confidence in the significant difference between the test substance and the control to diagnose HCC. The raw data used for statistical processing are the values analyzed in duplicate, triple or multiple for each marker.

This statistical analysis method is very useful for making clinically meaningful judgments through statistical processing of biomarkers as well as clinical and genetic data.

The method according to the invention can be used to determine the degree of severity of the HCC. For example, it can be assessed as mild HCC, moderate HCC or severe HCC compared to the profiles of the positive and negative controls. Furthermore, marker profile analysis for a certain HCC population may be performed and classified according to a certain criterion based on the profile result. In this way, early detection may prevent expensive tests such as magnetic resonance imaging (MRI).

In other embodiments, the method may be performed several times over a period of time, for example over a year, and may be used to monitor trends in expression patterns. Depending on the type of marker, an increase or decrease in expression may be associated with the state of the HCC. It can be used to determine HCC incidence, progression, exacerbation, etc., in comparison with previous test values or the value of the control group for the same subject. Based on the change in HCC markers over time, preventive measures can be taken to prevent progression to hepatocellular carcinoma or severe hepatocellular carcinoma. For the confirmation of HCC, biomarkers can be used as an aid, and other diagnostic methods such as AFP tests, ultrasound, computerized axial tomography (CT scan) or magnetic resonance imaging (MRI) Can be used with inspection.

Hereinafter, examples are provided to help understand the present invention. However, the following examples are provided only to more easily understand the present invention, and the present invention is not limited to the following examples.

Example

Example 1. Clinical Sample Information Used herein

The experiment was conducted in accordance with the protocol approved by the Medical Research Ethics Review Board of Seoul National University.

To select marker candidate groups, 60 normal subjects (male / female = 41/19) and 60 liver cancer patients (male / female = 42 /) were selected for the selection of protein candidates with expression differences in pooled samples of normal and liver cancer patients. The initial study was conducted with the sample of 18). In practice, the primary sample group was 30 patients with cirrhosis, 30 patients before liver cancer, 30 patients cured after liver cancer, and the second sample group was 50 patients with liver cirrhosis and 50 patients with liver cirrhosis. Samples were used for 50 patients cured after liver cancer treatment. Before and after liver cancer treatment, the samples were taken from the same patient, and serum samples were taken before treating liver cancer, and liver cancer did not recur or metastasize based on follow-up data for more than 6 months after treatment for liver cancer. Only samples of healthy liver cancer patients who were completely cured were included in the analysis.

Example 2 Data Mining for Target Candidate Selection

The candidate group of the target was selected as follows. The Liver Atlas database, the most comprehensive resource currently associated with liver disease, was used to identify candidate liver cancer early protein markers. A total of 50,265 proteins are known to be related to liver disease in the Liver Atlas database, and only those proteins that can be secreted or secreted into the blood (Uniprot database) are selected to select only proteins that can be detected in the blood. A total of 1,683 proteins were selected. Finally, peptide MS / MS using four different peptide MS / MS library sources (NIST Ion-Trap, NIST Q-TOF, ISB human plasma, Home made library) to select only proteins that can be detected by mass spectrometry. As a result, only 960 proteins were selected. Subsequently, detection target group selection was performed as follows. In fact, in order to select only targets that can be detected by mass spectrometry equipment, only peptides with proper signal detection through MRM analysis were selected from 60 normal groups and 60 HCC groups. , 1316 peptides were selected.

Example 3. Derivation of target marker candidate group through MRM analysis

Example 3-1. Serum Sample Preparation

Serum samples used in the experiment were prepared as follows. Blood was collected using a BD Vacutainer serum separation tube (BD, USA) (Silica clot activator, 10 mL, 16 × 100 mm; Product number = # 367820). Phenylmethyl sulfonyl fluoride (PMSF) was added to the collected blood to a final 1.0 mM. After 10 times of inverting mix, the supernatant was collected by centrifugation (maintaining 4 ° C.) at 3000 rpm for 10 minutes (after color observation, hemolyzed red sample was not used).

100 μL was aliquoted into 1.5 mL Eppendorf tubes, placed on ice immediately after dispensing, and labeled on the tubes (code label corresponding to sample group when labeling (eg NC-01, MC-01, PD-01, etc.). It was then immediately stored at −80 ° C. The procedure was carried out on ice and was all finished within 1 hour after blood collection.

Example 3-2. Serum Protein Depletion Treatment

In order to identify the markers present in the blood at low concentrations, the depletion process was performed to remove six proteins (albumin, IgG, IgA, haptoglobin, transferrin, alpha-1-antitrypsin) present in the blood at high-abundant levels. . By removing six large proteins, about 85% of the total protein mass was removed from the blood, and only the proteins corresponding to the remaining 15% protein mass were used for analysis. Depletion uses MARS (Multiple affinity removal system) (Agilent, USA) according to the manufacturer's method to remove the six most abundant proteins from the blood by binding them to the column and eluting small amounts of unbound proteins. Used for analysis.

Example 3-3. Peptides of Serum Proteins

Serum samples obtained after the depletion process were concentrated (w / 3K filter), and protein concentration was quantified by BCA (Bicinchoninic acid) analysis. 100 μg serum samples were taken and then treated with a final concentration of 6M urea / 20mM DTT (Tris pH 8.0), followed by incubation at 37 ° C. for 60 minutes. The final concentration of 50mM IAA (Iodoacetamide) was treated, and then incubated at room temperature for 30 minutes. 100 mM Tris pH 8.0 was treated so that the concentration of urea was 0.6 M or less. Trypsin treatment was performed so that the ratio of trypsin and serum was 1:50, and then incubated at 37 ° C. for 16 hours. The formic acid solution was treated to a final concentration of 5% and then subjected to the following desalting process.

Example 3-4. Desalination of serum proteins

Activation was performed by pouring 1 mL of 60% ACN / 0.1% formic acid three times on an OASIS column (Waters, USA). Equilibration was performed by pouring 1 mL of 0.1% formic acid five times into an OASIS column. Peptide samples were added and washed 5 times with 1 mL of 0.1% formic acid. Peptides were eluted with 1 mL of 40% ACN / 0.1% formic acid and 1 mL of 60% ACN / 0.1% formic acid. It was frozen at −70 ° C. for at least 1 hour and then dried by Speed-vac. The dried peptide sample was dissolved in 50 µl of Sol A buffer (3% ACN / 0.1% formic acid), centrifuged at 15,000 rpm for 60 min, and only 40 µl was transferred to the vial for analysis.

Example 3-5. MRM Analysis

For reproducible detection of 537 proteins and 1316 peptides selected as in Example 2 for MRM analysis, and in order to select targets showing differences in expression between the normal group and the HCC group, Samples in which 60 groups were pooled into 3 sets of 20 people and 60 samples in HCC group were combined into 3 sets of 20 people were each made, and repeated analysis was performed by MRM three times per set as described above.

MRM analysis was performed using Skyline (http://proteome.gs.washington.edu/software/skyline) for each target protein to select peptide and fragment ions for MRM analysis. Skyline is open source software for developing and analyzing MRM methods (Stergachis AB, et al., 2011, Nat Methods 8: 1041-1043).

In summary, full-length protein sequences were entered into Skyline in FASTA format and then designed as peptides to generate a product ion list, which was monitored by MRM. Peptide filter conditions used for transition selection were as follows: peptide maximum length was 30, minimum length was 6 amino acids and did not include repeated arginine (Arg, R) or lysine (Lys, K). If methionine (Met, M) was also included in the peptide, it was removed due to the possibility of modification. It was also not used when proline came after arginine or lysine, but when histidine (His, H) was included, the charge was changed but used.

Peptides satisfying these conditions were used as the Q1 transition. Quadruple 1 (Q1) served as a filter that can pass only certain Q1 m / z. Precursor ions that passed through the Q1 filter were fragmented by electrical energy in Quadruple 2 (collision cells), which were broken down into product ions. This product ion can pass only certain product ions through Quadruple 3 (Q3), which acts as a filter as in Quadruple 1 (Q1). The ions that passed through Quadruple 3 (Q3) were converted into digital signals at the detector and shown as peak chromatograms. The area of these peaks was analyzed for relative and absolute quantitative analysis. A file containing this information was entered into Analyst (AB SCIEX, USA) for MRM analysis and analyzed using nonscheduled MRM method. The MRM results were generated as wiff and wiff.scan files, which were converted into mzXML format using mzWiff and inputted into Skyline for MRM data processing to obtain peak intensities of MRM transitions. Figure 1 shows the process diagrammatically.

For the purpose of ensuring quantitation, all peptides are known to have a concentration that is normalized to the peak area of the peptide when the MRM is analyzed. This peptide is called an internal standard peptide, which is a stable isotope. It is a peptide having an amino acid containing. In this study, all samples were injected with LNVENPK, a heavy-labeled peptide from beta-galactosidase (lacZ) derived from E. coli, which does not exist in the human proteome. The peak area values of all target peptides from the MRM analysis were normalized to the peak area values of the corresponding internal standard peptides.

Through semi-quantitative MRM analysis as described above, two groups (normal) after repeated analysis three times per set to select only proteins / peptides reproducibly detectable by mass spectrometry (MRM-technique) As a result, only 347 proteins and 754 peptides were selected as reproducible targets.

Then, the p-value and fold-change level between the groups were checked in order to select a target with a difference in expression between the normal group and the HCC group as a reproducible target. In the case of normal distribution (p-value> 0.05) for reproducible targets (347-protein, 754-peptides) through normality test (Shaipro-Wilk), independent T-test In case of not following the normal distribution (p-value ≤ 0.05), the analysis was performed by the nonparametric Mann-Whitney test. In the case of the independent T-test, the value of p-value was identified by dividing it through the equal variance (p-value> 0.05) and the non-uniformity (p-value ≤ 0.05) through the Leevene's test Independent T-test and Mann-Whitney test confirmed that the target protein / peptide showed a significant difference (p-value ≤ 0.05) between the normal group and the HCC group.

In addition, candidate protein marker groups were further selected that exhibited a pattern of increase or decrease of fold-change levels of 1.5-fold or more between the normal and HCC groups. As a result, 195 proteins and 443 peptides with a p-value of 0.05 or less were identified between the normal group and the HCC group, and 191 proteins and 389 peptides with 1.5 fold-change or more were identified. 492 peptides were selected as target candidates.

Then, to confirm whether the selected 227 proteins and 492 peptides were unique peptides on the entire human proteome, the unique peptides were checked using NCBI's BlastP search program. Through this, 15 proteins corresponding to 37 non-unique peptides were excluded, and 216 proteins and 460 peptides were finally selected as a target candidate group, which was a unique peptide, showing a significant difference between the final normal group and the HCC group.

Example 4. Confirmation of Blood Endogenous Peptides of Target Candidate Markers

Whether the proteins and peptides selected in Example 3 were present in actual blood was confirmed as follows.

To do this, the sample was pooled together with 60 normal groups and 60 HCC groups using a stable-isotope labeled standard (SIS) peptide corresponding to 216 proteins. Reconfirmed whether or not the peptide was correct. The SIS peptide is a peptide in which ¹² C and ¹⁴ N of amino acids lysine (Lys, K) or arginine (Arg, R) at the peptide C-terminus are substituted with ¹³ C and ¹⁵ N. It differs from the endogenous peptides present in the blood, but because they have the same sequence, they have the same peptide hydrophobicity, so they elute at the same time (RT) as the peptides in the blood on the chromatogram. (See FIG. 2).

In the MRM analysis, the Q3 intensity pattern of the SIS peptide and the blood endogenous peptide was analyzed to determine whether the complex blood sample exhibited signal interference by a peptide other than the target peptide. It confirmed (FIG. 3).

To determine whether a signal interference occurs, the automated detection of inaccurate and imprecise transitions (AuDIT) programs are used to compare the relative generated ion intensities of the peptides in the blood and the SIS peptide (P-value threshold: 0.05). And whether the peak area value of the SIS peptide is constantly detected during repeated measurements (CV threshold: 0.2) was confirmed. Through this, 123 proteins and 231 peptides were finally selected as endogenous target proteins / peptides of quantifiable blood without signal interference.

Example 5. Identification of Levels of Blood Endogenous Peptides

As in Example 4, the concentrations present in the blood were determined for quantifiable blood endogenous targets (123 proteins and 231 peptides). MRM analysis was performed on a sample in which 231 SIS peptide mixtures were sequentially diluted to 3-point (20 nM, 200 nM, 2000 nM) in 10 μg of a sample containing 60 normal groups and 60 HCC groups. Signals for endogenous peptides and complementary synthetic (SIS) peptides were identified.

Calculate the relative ratios of the peak area values of peptides in the blood and the peak area values of complementary SIS peptides (3-points) for only the generated ions (Q3) without interference. And, the amount of SIS-peptide injected was multiplied to finally confirm the level in blood for the target peptide (see FIG. 4).

The blood levels of the 231 peptides revealed that the lowest concentration of protein in the serum was ISLR (Immunoglobulin superfamily containing leucine-rich repeat protein), with a concentration of 0.15-fmol / μg and the highest concentration. The identified protein is A2MG (Alpha 2 macroglobulin), the concentration was found to be 5.57-pmol / μg (Dynamic range: 3.7 × 10 ^ 4 order) (Fig. 5).

As a result of measuring the concentration of endogenous peptides in the blood, the low-abundance targets with blood concentrations of 20-fmol / μg or less were found to be 34 proteins and 36 peptides, and 20-fmol / μg and 2000-fmol Middle-abundance targets measured between / μg were 93 proteins and 174 peptides, and high-abundance targets measured above 2000-fmol / μg were 11 proteins and 22 peptides. It was confirmed to be (FIG. 6).

Example 6 Derivation of Liver Cancer Early Diagnosis Marker Candidates through Actual Individual Sample Application

Since about 90% of liver cirrhosis patients are diagnosed with liver cancer, liver cirrhosis patients can be classified as a high risk group for liver cancer. there is a problem. Therefore, if the amount of protein showing the difference between liver cirrhosis patients and liver cancer patients can be confirmed in advance by MRM technique, early diagnosis can be made in advance of patients progressing from liver cirrhosis patients to liver cancer patients.

Validated endogenous peptides in blood were determined for SIS peptide injection concentrations complementary thereto. For low abundance targets (level in blood <20-fmol / μg), all were injected with 20-fmol SIS-peptides in batch, and middle-abundance targets (20-fmol / μg < For blood level <2000-fmol / μg), the amount of SIS peptide was injected equal to the amount of peptide in the blood, and for high-abundance targets (blood level> 2000-fmol / μg) all 2000-fmol amount of SIS-peptide was injected.

For the ISLR protein with the lowest level in blood, 20-fmol of SIS-peptide was injected up to 13 times higher than the level in the blood, with the highest level of A2MG protein. In the case of injecting 2000-fmol of SIS-peptide, a low amount diluted up to 1/28 times compared to the level in blood was injected (FIG. 7).

As a primary sample, 30 patients with cirrhosis, 30 patients with hepatocarcinoma, and 30 patients with hepatocarcinoma after the onset of liver cancer were used as targets for early diagnosis of target candidate groups (123 proteins and 231 peptides). As the primary sample, the markers for diagnosing liver cancer with the difference in the three groups were derived by using samples of 30 patients with liver cirrhosis, 30 patients with liver cancer, and 30 patients with complete cure.

After blinding, the MRM analysis sequence was randomly analyzed so that the experimenter could not identify the patient group, and the analysis was repeated three times per sample. The peak area values of the target peptides thus obtained are normalized to the peak area values of the SIS peptides complementary to each other, and then IBM SPSS statistics (version 21.0) and GraphPad (version 6.00) are obtained. I went through the analysis.

Example 7 Liver Cancer Early Diagnosis Single Marker Group Derivation

The early diagnosis by conventional alpha-fetoprotein (AFP) is difficult to distinguish between chronic liver disease (chronic hepatitis and cirrhosis) and liver cancer, and thus cannot be used for early diagnosis of liver cancer, suggesting a new early diagnosis marker to replace it. For this purpose, we analyzed targets showing differences in expression between the LC (cirrhosis) group and the HCC group and between the HCC group and the Recovery group (the LC group with liver cancer). The results are shown in Table 1 below.

As a result of analysis with a training set to identify a single marker candidate group for which the diagnostic ability can be applied in the actual clinical area (hospital) for the purpose of early diagnosis of liver cancer (check whether the same expression tendency is found in the independent sample group). In the analysis of the test sample and the secondary sample (Test set), a marker with a high AUC value was identified in common. As a result, there was a significant difference between the LC group, the HCC group, the HCC group and the recovery group (P-value ≤ 0.05). ) And a high diagnostic (AUC-value> 0.700) target was finally identified as 23 proteins and 51 peptides derived from the protein (Tables 1-1 and 1-2). Tables 1-1 and 1-2 show target proteins and peptides with an AUC value of 0.700 or higher after training set / test set sample analysis. In the following table, red indicates a case where the expression level is increased in the comparison between groups, and blue indicates a case where the expression level is decreased.

Table 1-1

TABLE 1-2

Example 8. Liver Cancer Multiple Marker Derivation

23-protein, 51-peptide showed high diagnostic ability in the LC group, the HCC group, the HCC group, and the recovery group in the primary and secondary test sets as in Example 7. Multi-variate Analysis (MA) was used to build and compare multiple protein marker panels.

One panel analysis using logistic regression, one of the statistical methods, showed that four panels of peptides (ALS + ITIH1 + THRB + FINC) had the highest diagnostic power when comparing LC and HCC groups. It was confirmed that the combination was visible, and two panels of peptides (THRB, FINC) were found to be the most diagnostic combinations between the HCC group and the recovery group. Specifically, when comparing between the LC group and the HCC group, the four peptide panels identified had an AUC value of 0.994, which means 57 out of 60 liver cirrhosis patients (95.0% of cirrhosis patients) and 60 liver cancer patients. Among 58 patients (Accuracy 96.7%) can be diagnosed as liver cancer patients, the diagnostic accuracy using the panel of four peptide (4-protein) markers was confirmed as 95.8% (FIG. 8). In addition, when the remaining 19-protein (47-peptide) is added to the 4-protein (4-peptide), it was confirmed that it is possible to make a myriad of protein combinations of AUC 0.990 or more.

In comparison between the HCC group and the recovery group, the two panels of peptides identified had an AUC value of 0.957 (FIG. 9), representing 53 out of 60 liver cancer patients (Accuracy 88.3%) and 60 in total. After liver cancer treatment, 52 patients (Accuracy 86.7%) could be diagnosed as liver cancer patients, and the accuracy of diagnosis using the two peptide marker panels was 87.5%. In addition, when the remaining 21-protein (49-peptide) is added to the corresponding 2-protein (2-peptide), it was confirmed that it is possible to make a myriad of protein combinations of AUC 0.950 or more.

Example 9 Further Validation with Antibodies to Liver Cancer Markers

Markers according to the present application can be detected at the protein level, the detection method at the protein level may include the analysis using the MRM analysis and the antibody used in the present embodiment, only one, that is, the MRM analysis alone provides the desired result according to the present application. You can either get it or use both methods for reconfirmation.

In order to reconfirm through antibody analysis, 7 proteins having a higher AUC value were selected from 23 proteins verified by MRM analysis (Peptide Level) as a liver cancer diagnosis marker, and an analysis using an antibody was further performed (Table 2).

Since antibodies recognize antigens consisting of some residues in the whole protein, the antibody selection criteria allowed the peptide portion analyzed by MRM to be included (or as close as possible) in the antibody immunogen portion and for plasma / serum. Those with Western blotting results and the presence of monoclonal antigen were selected first, followed by alpha-2-antiplasmin (A2AP) (Mouse monoclonal), AMBP (Rabbit monoclonal), AFP (alpha-fetoprotein) (Mouse monoclonal) , FETUA (Rabbit monoclonal), FINC (fibronectin) (Rabbit monoclonal), ITIH1 (Inter-alpha-trypsin inhibitor heavy chain (Mouse polyclonal), ITIH3 (Inter-alpha-trypsin inhibitor heavy chain H3) (Goat polyclonal), PLGA (PLGA) Plasminogen-related protein A) (Rabbit polyclonal) Western blots were performed using the antibodies shown in parentheses against THRB (Prothrombin) (Rabbit monoclonal) (Santa Cruz Biotechnology, USA).

For this purpose, 12 samples per 3 groups (LC, HCC, and recovery) were selected, using an independent sample group that was not used for primary and secondary MRM analysis. The selected HCC / recovery group samples were selected from Stage I (early liver cancer) samples, and normalized to O.D (absorbance) measured in a control (loading control) and subjected to SDS-PAGE gel deviation correction. As a control, mouse monoclonal antibodies against betaactin and transferrin were used.

The results are described in FIG. These results indicate that the marker according to the present application can be analyzed through various protein analysis methods.

Subsequently, an ELISA assay (Protein Level, Native form) was further performed for the purpose of applying / utilizing a target protein (FINC) having the highest distinction between groups in Western blot using the antibody in actual clinical practice. Twenty samples per 3-group (LC, HCC, and Recovery) were selected to perform ELISA analysis, using independent sample groups not used for primary and secondary MRM analysis.

As a result of using the ELISA (Uscn Life Science Inc.Korea) kit for the target protein (FINC) according to the manufacturer's method, the expression pattern between sample groups was consistent with the results of MRM and Western blotting analysis. The AUC value was 0.832 when comparing the group with the liver cancer group, and the AUC value was 0.658 when comparing the liver cancer group with the liver cancer recovery group (Table 2). These results indicate that the markers according to the present application can be developed and used with various protein assay kits such as MRM, Western blot, ELISA.

[Table 2] ELISA Analysis Information

Claims (17)

1. Insulin-like growth factor-binding protein complex acid labile subunit (ALS); AMBP (Alpha-1-microglobulin / bikunin precursor); CPB2 (Carboxypeptidase B2); CHLE (Cholinesterase); CLUS (Clusterin); CNDP1 (Beta-Ala-His dipeptidase); CO6 (Complement component C6); CPN2 (Carboxypeptidase N subunit 2); FA11 (Coagulation factor XI); FINC (Fibronectin); Hepatocyte growth factor activator (HGFA); Insulin-like growth factor-binding protein 3 (IBP3); Insulin-like growth factor II (IGF2); ITIH1 (inter-alpha-trypsin inhibitor heavy chain H1); KAIN (Kallistatin); KLKB1 (Plasma kallikrein); Phosphatidylcholine-sterol acyltransferase (LCAT); PLGA (Plasminogen-related protein A); PLMN (Plasminogen); THRB (Prothrombin); VTDB (Vitamin D binding protein); And detection reagents for detecting one or more biomarkers selected from the group consisting of VTNC (Vitronectin).
2. The method of claim 1,

   The biomarker is selected from the group consisting of ALS, CBPB2, CLUS, CNDP1, CPN2, FA11, FINC, and HGFA, liver cancer diagnosis or prognostic composition.
3. The method of claim 1,

   The biomarker is FINC; Combination of THRB and FINC; Or ALS, ITIH1, THRB and FINC, a composition for diagnosing or diagnosing liver cancer.
4. The method of claim 1,

   The liver cancer prognosis is to determine whether to recover after liver cancer treatment, liver cancer diagnosis or prognostic composition for measuring.
5. The method of claim 4, wherein the biomarker for determining recovery after treatment is selected from the group consisting of THRB, FINC, ITIH1, IBP3, PLMN, CBPB2 and PLGA, liver cancer diagnosis or prognostic composition.
6. The method of claim 1,

   The detection reagent is a reagent that can detect the marker at the protein or nucleic acid level, liver cancer diagnostic or prognostic composition.
7. The method of claim 6,

   The reagent for detecting the protein level is Western blot, ELISA, radioimmunoassay, immunodiffusion, immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, FACS, mass spectrometry, or a reagent for protein microarray,

   The detection reagent of the nucleic acid level is nucleic acid amplification reaction, polymerase chain reaction, reverse transcription polymerase chain reaction, competitive polymerase chain reaction, Nuclease protection assay (RNase, S1 nuclease assay), in situ hybridization, nucleic acid microarray or northern Liver cancer diagnosis or prognostic composition, which is a reagent for lot.
8. The method of claim 7, wherein

   The protein level detection reagent is an antibody, antibody fragment, aptamer, avider or peptidomimetics, receptor, ligand that specifically recognizes the full length or fragment thereof of the marker. Or includes a cofactor,

   The nucleic acid level detecting reagent includes a nucleic acid sequence of the marker, a nucleic acid sequence complementary to the nucleic acid sequence, a primer or a probe or a primer and a probe specifically recognizing the nucleic acid sequence and fragments of the complementary sequence. , Liver cancer diagnosis or prognostic composition.
9. The method of claim 1,

   Said composition is for ELISA analysis, dip stick rapid kit analysis, MRM analysis, microarray, nucleic acid amplification, or immunoassay, liver cancer diagnosis or prognostic composition.
10. The method of claim 9,

    The composition is for MRM analysis, the peptide of each marker used in the MRM analysis is that described in Tables 1-1 and 1-2, liver cancer diagnosis or prognostic composition.
11. The method according to any one of claims 1 to 10,

    Wherein the composition is in addition to the biomarker further comprises an AFP biomarker, liver cancer diagnostic or prognostic composition.
12. To provide information necessary for the diagnosis or prognosis of liver cancer,

    Insulin-like growth factor-binding protein complex acid labile subunit (ALS) from biological samples from the subject; AMBP (Alpha-1-microglobulin / bikunin precursor); CPB2 (Carboxypeptidase B2); CHLE (Cholinesterase); CLUS (Clusterin); CNDP1 (Beta-Ala-His dipeptidase); CO6 (Complement component C6); CPN2 (Carboxypeptidase N subunit 2); FA11 (Coagulation factor XI); FINC (Fibronectin); Hepatocyte growth factor activator (HGFA); Insulin-like growth factor-binding protein 3 (IBP3); Insulin-like growth factor II (IGF2); ITIH1 (inter-alpha-trypsin inhibitor heavy chain H1); KAIN (Kallistatin); KLKB1 (Plasma kallikrein); Phosphatidylcholine-sterol acyltransferase (LCAT); PLGA (Plasminogen-related protein A); PLMN (Plasminogen); THRB (Prothrombin); VTDB (Vitamin D binding protein); And detecting the presence and / or concentration of nucleic acids and / or proteins of at least one biomarker selected from the group consisting of VTNC (Vitronectin);

    Comparing the detection result for the level or presence of the nucleic acid or protein with the corresponding result of the corresponding marker in the control sample; And

    Compared to the control sample, if there is a change in the nucleic acid or protein concentration of the subject sample, or if there is a change in the presence or absence of the nucleic acid or protein, determining the liver cancer marker in vitro, comprising determining the liver cancer How to detect.
13. The method of claim 12,

    The biological sample is whole blood, serum or plasma,

    Wherein said control sample is a sample from a patient who has been cured after treatment with normal, hepatitis, cirrhosis or liver cancer.
14. The method of claim 12,

    Wherein said biomarker is selected from the group consisting of ALS, CBPB2, CLUS, CNDP1, CPN2, FA11, FINC, and HGFA.
15. The method of claim 12,

    Wherein the method further comprises an AFP biomarker in addition to the one or more markers.
16. The method of claim 12,

    The method is carried out by protein or nucleic acid microarray analysis, nucleic acid amplification, antigen-antibody reaction, or mass spectrometry.
17. The method of claim 12,

    The liver cancer diagnosis includes the diagnosis of a patient progressing from cirrhosis to liver cancer,

    The liver cancer prognosis method comprises determining whether to recover after liver cancer treatment.

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