Diagnostic marker for liver fibrosis and use thereof

Abstract

This application relates to the field of medical testing technology, specifically to a biomarker, diagnostic method, and application for the diagnosis of liver fibrosis. Through plasma proteomics analysis of patients with liver fibrosis, this application has discovered and validated a series of biomarkers and combinations thereof with clinical value for the early diagnosis of liver fibrosis.

Description

Technical Field

[0001] This application relates to the field of medical testing technology, specifically to diagnostic markers for liver fibrosis, combinations of markers, and diagnostic methods and applications for liver fibrosis. Background Technology

[0002] Nonalcoholic fatty liver disease (NAFLD) is a metabolic stress-induced liver injury closely related to insulin resistance and genetic susceptibility. Its spectrum includes simple fatty liver (NAFL), nonalcoholic steatohepatitis (NASH), and NASH-related liver fibrosis and cirrhosis. Hepatic fibrosis (HF) is a pathological process of extracellular matrix accumulation during tissue regeneration following damage to the liver structure, leading to fibrosis. Further progression of liver fibrosis leads to cirrhosis, a common chronic progressive liver disease and a leading cause of death. Therefore, early diagnosis of liver fibrosis can effectively prevent its progression to cirrhosis and has significant clinical value.

[0003] Common methods for detecting liver fibrosis include blood biochemistry tests (liver function-related aspartate aminotransferase (AST) and alanine aminotransferase (ALT), APRI score, FIB-4 index, etc.), imaging tests (ultrasound, CT, and MRI), and pathological tests (puncture biopsy). Currently, blood biochemistry tests lack highly sensitive and specific indicators for liver fibrosis, and the accuracy of a single indicator in assessing liver fibrosis is not high; multiple tests are usually required to improve diagnostic performance to a limited extent. Imaging tests can typically only detect severe liver fibrosis and are not very useful for assessing early fibrosis. While pathological testing is the gold standard for diagnosing liver fibrosis, it is complex, expensive, invasive, and can cause pain, bleeding, or even death in clinical applications. Furthermore, because liver fibrosis is unevenly distributed due to many diseases, if a sample is not obtained from the fibrotic area during liver biopsy, a missed diagnosis may occur. In conclusion, the development and validation of new diagnostic tools for liver fibrosis have significant clinical and social value.

[0004] Proteomics is the science that studies the composition, localization, and dynamic changes of proteins in cells, tissues, or organisms, including the study of protein expression patterns and proteomic functional patterns. In recent years, with the development of proteomics technologies, high-performance liquid chromatography-high-resolution tandem mass spectrometry (HPLC-MS / MS) has gradually become the mainstream technique in proteomics. Although an increasing number of studies have reported on the identification of disease-diagnostic biomarkers based on proteomics, the vast majority remain at the laboratory research stage, with very few reaching clinical application. Moreover, to achieve high sensitivity and specificity in disease detection, the performance of a single biomarker often cannot meet clinical requirements; only by using combined assays can the accuracy of diagnosis be further improved. Therefore, discovering new biomarkers related to the early diagnosis of liver fibrosis and constructing early predictive models by combining multiple biomarkers has significant clinical value. In view of this, this application is submitted. Invention Overview

[0005] To address the aforementioned clinical challenges, this application utilizes high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS / MS) to perform plasma proteomic analysis on patients with liver fibrosis. Based on high-throughput proteomics, relevant protein biomarkers for the early diagnosis of liver fibrosis are identified. Furthermore, multiple biomarkers are combined to construct a diagnostic model for liver fibrosis, which has clinical significance for the early diagnosis and treatment of liver fibrosis, including conditions such as non-alcoholic fatty liver disease.

[0006] Therefore, this application includes at least the following objectives:

[0007] The primary objective of this application is to identify biomarkers that are diagnostically significant for liver fibrosis.

[0008] The second objective of this application is to seek novel diagnostic methods for liver fibrosis, especially for early diagnosis.

[0009] To achieve the above objectives, this application proposes the following specific technical solutions:

[0010] This application first provides a biomarker for the diagnosis of liver fibrosis, said biomarker including any one, multiple or all of C7, PIGR, LGALS3BP, VCAM1, VWF, ALCAM, TGFBI, CNDP1, IGFALS, IGJ, EFEMP1, A2M, FCGBP, SHBG, COLEC11, IL1RAP, IGFBP3, FAP, PVR, ANPEP, PON3, FBLN1, FCGR3B, LYVE1, C3, SERPINA1, IGHM, C1QA, ADIPOQ, A30, LEPR, CAT, APOC1, DSG1, CRP, S100A8, CDH1, CSF1R, IGHV5-51, IGHV1-24, SERPINA11, MET and / or CD5L.

[0011] In some aspects, the marker is one; in some aspects, the marker is two; in some aspects, the marker is three; in some aspects, the marker is four; in some aspects, the marker is five; in some aspects, the marker is six; in some aspects, the marker is seven; in some aspects, the marker is eight; without limitation, this application includes combinations of multiple markers.

[0012] This application also provides a product for the diagnosis of liver fibrosis, the product comprising the above-described detection reagents or components for detecting the above-described biomarkers.

[0013] This application also provides the use of a detection reagent or component for obtaining the level of the above-mentioned biomarkers in a sample in the preparation of a liver fibrosis diagnostic product, or the use of the above-mentioned biomarkers in the diagnosis of liver fibrosis.

[0014] In some respects, the products include, but are not limited to, reagent kits, system devices, computer-readable media, or computer systems.

[0015] In some respects, the level includes the nucleic acid level or the protein level; wherein the nucleic acid level is particularly the transcriptional level of nucleic acids.

[0016] In some respects, the nucleic acid level or protein level includes, but is not limited to, the abundance or concentration of nucleic acids or proteins.

[0017] Furthermore, the nucleic acid level can be obtained through sequencing technology, nucleic acid amplification technology, nucleic acid hybridization technology, electrophoresis technology, biomolecular mass spectrometry technology, or chromatography technology.

[0018] Furthermore, the methods for obtaining nucleic acid levels include, but are not limited to, any of the following: gene sequencing, polymerase chain reaction, isothermal amplification reaction, gene chip, probe hybridization, gel electrophoresis, RNA blotting, nucleic acid mass spectrometry, or liquid chromatography.

[0019] Furthermore, the protein levels are obtained using sequencing, immunoassay, electrophoresis, biomolecular mass spectrometry, or chromatography.

[0020] Furthermore, the protein level acquisition methods include, but are not limited to, any of the following: amino acid sequencing, enzyme-linked immunosorbent assay (ELISA), chemiluminescence immunoassay, immunochromatography, radioimmunoassay, immunohistochemistry, Western blotting, flow cytometry, gel electrophoresis, adjacent extension analysis (PEA), SomaScan based on nucleic acid aptamers, proteometry, or liquid chromatography.

[0021] In some aspects, the product may also include a treatment reagent for the sample to be tested, which may include at least one of a sample lysis reagent, a sample purification reagent, and a sample extraction reagent.

[0022] In some respects, the product may also include at least one or more of standards, calibrators, control standards, and buffer solutions.

[0023] In some respects, the samples include, but are not limited to, any one or more of the following: tissues, cells, body fluids, serum, plasma, whole blood (peripheral blood), urine, semen, saliva, pleural effusion, ascites, cerebrospinal fluid, feces, or synovial fluid.

[0024] This application also provides a method for diagnosing liver fibrosis in vivo or in vitro, including the step of acquiring the levels of the aforementioned biomarkers in a subject sample.

[0025] In some respects, the method specifically includes the following steps:

[0026] (i) Obtain the levels of the above biomarkers in the subject samples;

[0027] (ii) Comparison with the levels of the corresponding biomarker in the control sample; wherein a significant difference in the biomarker levels between the subject sample and the control sample is an indication that the subject has or will have liver fibrosis; or

[0028] (ii) Compare with a set absolute threshold; wherein, a level of the biomarker in the subject sample that is higher than the absolute threshold is an indication that the subject has or will have liver fibrosis.

[0029] In some respects, to improve the specificity and sensitivity of the overall diagnosis, the method preferably includes the joint detection of multiple biomarkers; of course, it can also be combined with other biomarkers or physiological and biochemical indicators known in the prior art.

[0030] In some respects, the level includes the nucleic acid level or the protein level; preferably, the nucleic acid level is particularly the transcriptional level of nucleic acids.

[0031] In some respects, the nucleic acid level or protein level includes, but is not limited to, the abundance level or concentration level of nucleic acid or protein.

[0032] Furthermore, the nucleic acid level is obtained through sequencing technology, nucleic acid amplification technology, nucleic acid hybridization technology, electrophoresis technology, biomass spectrometry technology, or chromatography technology. Even further, the methods for obtaining the nucleic acid level include, but are not limited to, any of the following: gene sequencing, polymerase chain reaction, isothermal amplification reaction, gene chip method, probe hybridization, gel electrophoresis, RNA blotting, nucleic acid mass spectrometry, or liquid chromatography.

[0033] Furthermore, the protein level is obtained using sequencing, immunoassay, electrophoresis, mass spectrometry, or chromatography. Even further, the methods for obtaining the protein level include, but are not limited to, any of the following: amino acid sequencing, enzyme-linked immunosorbent assay (ELISA), chemiluminescence immunoassay, immunochromatography, radioimmunoassay, immunohistochemistry, Western blotting, flow cytometry, gel electrophoresis, proteometry, or liquid chromatography. In some preferred embodiments, the protein level is obtained particularly based on antibody immunological methods.

[0034] In some respects, the subjects include, but are not limited to: mammals, non-primate mammals (e.g., cattle, pigs, camels, llamas, horses, goats, rabbits, sheep, hamsters, guinea pigs, cats, dogs, rats, mice, etc.) or non-human primates (e.g., monkeys, chimpanzees, humans); preferably, the subjects are humans.

[0035] In some respects, the samples include, but are not limited to: tissues, cells, body fluids, serum, plasma, whole blood (peripheral blood), urine, semen, saliva, pleural effusion, ascites, cerebrospinal fluid, feces, or synovial fluid.

[0036] In some respects, any of the aforementioned diagnoses refer to the early diagnosis of liver fibrosis, which is achieved, in particular, by distinguishing between no significant liver fibrosis (corresponding to F0-F1 grades) and significant liver fibrosis (corresponding to F2-F4 grades).

[0037] In other aspects, any of the aforementioned diagnoses may also refer to the stratification or grading of the severity of liver fibrosis; the stratification or grading is particularly used to distinguish the severity of different grades of liver fibrosis; preferably, the severity includes no significant liver fibrosis (corresponding to F0-F1 grades) and significant liver fibrosis (corresponding to F2-F4 grades); it also includes different grades of significant liver fibrosis (F2-F4 grades).

[0038] This application also provides a treatment method for liver fibrosis, the method comprising any of the aforementioned diagnostic methods, and further comprising the step of administering medication to a subject based on the diagnostic result.

[0039] In some respects, the liver fibrosis described above can be present in a variety of diseases, including but not limited to: viral hepatitis, fatty liver (including non-alcoholic fatty liver disease and alcoholic liver disease), autoimmune diseases, and hepatic encephalopathy.

[0040] This application also provides a method for detecting biomarkers in subjects who have or are suspected of having liver fibrosis in vivo or in vitro, the method comprising determining or detecting the levels of the aforementioned biomarkers in a sample from the subject.

[0041] In some respects, the method can be used for disease diagnosis, but it can also be used for non-disease diagnosis, especially when used for in vitro testing.

[0042] This application also provides a method for evaluating or screening diagnostic biomarkers for liver fibrosis, comprising the step of performing a correlation or consistency analysis between a potential biomarker and the aforementioned biomarker, and verifying the diagnostic value of the potential biomarker through the conclusion of the correlation or consistency; for example, when the potential biomarker is correlated or consistent with the biomarker described in this application, the potential biomarker can be used as a candidate diagnostic biomarker for liver fibrosis.

[0043] This application also provides a method for screening liver fibrosis treatment agents in vivo or in vitro, the method including the step of evaluating the levels of the above-mentioned biomarkers in samples treated with the treatment agent; for example, when the levels of the biomarkers change significantly before and after treatment with the treatment agent, it indicates that the treatment agent can be used as a potential treatment agent for liver fibrosis.

[0044] The beneficial technical effects of this application are as follows:

[0045] The early diagnostic biomarkers or biomarker combinations for liver fibrosis screened in this application have excellent ability to differentiate and diagnose liver fibrosis, and are suitable for clinical application. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 A flowchart of the discovery and verification process for the markers in this application;

[0048] Figure 2 A heatmap of plasma protein expression was obtained in the cohort; where liver fibrosis represents liver fibrosis; F0 represents no liver fibrosis, F1 represents mild liver fibrosis, F2 represents moderate liver fibrosis, F3 represents severe liver fibrosis, and F4 represents cirrhosis.

[0049] Figure 3 Volcano plot of differentially expressed proteins in plasma of patients with liver fibrosis grades 0-1 and 2-4 in the cohort was discovered. Invention Details

[0050] This application discloses applications in the diagnosis of liver fibrosis. It is particularly important to note that all similar substitutions and modifications will be obvious to those skilled in the art and are considered to be included within this application. The methods and applications of this application have been described through preferred embodiments. Those skilled in the art will readily be able to modify or appropriately alter and combine the preparation methods and applications described herein without departing from the content, spirit, and scope of this application to implement and apply the technology of this application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0051] The following basic terms or definitions are provided merely to aid in understanding this application. These definitions should not be construed as having a scope less than that understood by those skilled in the art. Unless otherwise defined below, all technical and scientific terms used in the specific embodiments of this application are intended to have the same meaning as commonly understood by those skilled in the art. While it is believed that the following terms will be well understood by those skilled in the art, the following definitions are still set forth to better explain this application.

[0052] The terms “comprising,” “including,” “having,” “containing,” or “involving” are inclusive or open-ended and do not exclude other unlisted elements or method steps. The term “consisting of” is considered a preferred embodiment of the term “comprising.” If a group is defined below as including at least a certain number of embodiments, this should also be understood as disclosing a group that preferably consists only of those embodiments.

[0053] When referring to a singular noun, the indefinite or definite article used, such as "a" or "a kind of," "the," includes the plural form of the noun.

[0054] Furthermore, the terms first, second, third, (a), (b), (c), and similar terms used in the specification and claims are for distinguishing similar elements and are not necessary for the order of description or chronological sequence. It should be understood that such terms are interchangeable in appropriate contexts, and the embodiments described herein can be implemented in a different order than that described or illustrated herein.

[0055] The term “and / or” is considered as a specific disclosure of each of the two specified features or components having or not having the other. Thus, the term “and / or” as used herein in phrases such as “A and / or B” is intended to include A and B; A or B; A (alone); and B (alone). Similarly, the term “and / or” as used in phrases such as “A, B and / or C” is intended to cover each of the following: A, B and C; A, B or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

[0056] The terms “for example” and “that is” are used only as examples and are not intended to be limiting, and should not be interpreted as referring only to those items explicitly listed in the specification.

[0057] Terms such as "or more," "at least," and "more than," for example, "at least one," should be understood to include, but are not limited to, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 or 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000 or more of the stated values. This also includes any larger numbers or fractions in between.

[0058] Conversely, the term "not exceeding" includes every value less than the stated value. For example, "not exceeding 100 nucleotides" includes 100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 5 4, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, and 0 nucleotides. Also includes any smaller numbers or fractions in between.

[0059] The terms "multiple," "at least two," "two or more," and "at least the second" should be understood to include, but are not limited to, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, and 70. 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 or 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000 or more. Also includes any larger numbers or fractions in between.

[0060] The terms "approximately" and "generally" indicate an accuracy range that, as would be understood by those skilled in the art, still guarantees the technical effect of the features in question. This term typically indicates a deviation from the indicated value of ±10%, preferably ±5%.

[0061] As stated in the text, unless otherwise specified, any concentration range, percentage range, ratio range, or integer range should be understood to include any integer value within the range stated, and, where appropriate, its fractions (e.g., one-tenth and one-hundredth of an integer).

[0062] Reference will now be made to detailed embodiments of this application, one or more of which are described below. Each example is provided for explanation and not for limitation. It will be apparent to those skilled in the art that various modifications and variations can be made to this application without departing from its scope or spirit. For example, features described or illustrated as part of one embodiment may be used in another embodiment to produce further embodiments. Therefore, this application is intended to cover such modifications and variations falling within the scope of the appended claims and their equivalents. Other objects, features, and aspects of this application are disclosed in or will be apparent from the following detailed description. Those skilled in the art will understand that this discussion is merely a description of exemplary embodiments and is not intended to limit the broader aspects of this application.

[0063] Diagnostic uses of this application

[0064] This application identifies and validates effective biomarkers or combinations of biomarkers for liver fibrosis based on proteomics differential mining. Therefore, these biomarkers or combinations of biomarkers can be applied to the diagnosis of liver fibrosis or related diseases. Consequently, this application includes at least the following uses:

[0065] The application of detection agents or components for obtaining biomarker levels in individual or subject samples in the preparation of products for the diagnosis of liver fibrosis;

[0066] Application of obtaining biomarker levels in individual or subject samples in the diagnosis of liver fibrosis;

[0067] Application of biomarkers in the diagnosis of liver fibrosis;

[0068] In this application, the markers include any one, multiple, or all of the following: C7, PIGR, LGALS3BP, VCAM1, VWF, ALCAM, TGFBI, CNDP1, IGFALS, IGJ, EFEMP1, A2M, FCGBP, SHBG, COLEC11, IL1RAP, IGFBP3, FAP, PVR, ANPEP, PON3, FBLN1, FCGR3B, LYVE1, C3, SERPINA1, IGHM, C1QA, ADIPOQ, A30, LEPR, CAT, APOC1, DSG1, CRP, S100A8, CDH1, CSF1R, IGHV5-51, IGHV1-24, SERPINA11, MET, and / or CD5L.

[0069] In some embodiments, the marker is one, such as any one of C7, PIGR, LGALS3BP, VCAM1, VWF, ALCAM, TGFBI, CNDP1, IGFALS, IGJ, EFEMP1, A2M, FCGBP, SHBG, COLEC11, IL1RAP, IGFBP3, FAP, PVR, ANPEP, PON3, FBLN1, FCGR3B, LYVE1, C3, SERPINA1, IGHM, C1QA, ADIPOQ, A30, LEPR, CAT, APOC1, DSG1, CRP, S100A8, CDH1, CSF1R, IGHV5-51, IGHV1-24, SERPINA11, MET, or CD5L.

[0070] In some embodiments, the marker is a combination of two, such as: C7+PIGR, LGALS3BP+PIGR, VCAM1+PIGR, VWF+PIGR, ALCAM+PIGR, TGFBI+PIGR, CNDP1+PIGR, IGFALS+PIGR, IGJ+PIGR, EFEMP1+PIGR, A2M+PIGR, FCGBP+PIGR, SHBG+PIGR, COLEC11+PIGR, IL1RAP+PIGR, IGFBP3+PIGR, FAP+PIGR, PVR+PIGR. GR, ANPEP+PIGR, PON3+PIGR, FBLN1+PIGR, FCGR3B+PIGR, LYVE1+PIGR, C3+PIGR, SERPINA1+PIGR, IGHM+PIGR, C1QA+PIGR, ADIPOQ+ PIGR, A30+PIGR, LEPR+PIGR, CAT+PIGR, APOC1+PIGR, DSG1+PIGR, CRP+PIGR, S100A8+PIGR, CDH1+PIGR, CSF1R+PIGR, IGHV5-51+PI GR, IGHV1-24+PIGR, SERPINA11+PIGR, MET+PIGR or CD5L+PIGR; another example: PIGR+C7, LGALS3BP+C7, VCAM1+C7, VWF+C7, ALCAM+C7, TGFBI+C 7. CNDP1+C7, IGFALS+C7, IGJ+C7, EFEMP1+C7, A2M+C7, FCGBP+C7, SHBG+C7, COLEC11+C7, IL1RAP+C7, IGFBP3+C7, FAP+C7, PVR+C7, ANPEP+C7, PON3+C7, FBLN1+C7, FCGR3B+C7, LYVE1+C7, C3+C7, SERPINA1+C7, IGHM+C7, C1QA+C7, ADIPOQ+C7, A30+C7, LEPR+C7, CAT+C7, APOC1+C7, DSG1+C7, CRP+C7, S100A8+C7, CDH1+C7, CSF1R+C7, IGHV5-51+C7, IGHV1-24+C7, SERPINA11+C7, MET+C7, or CD5L+C7. This is not an exhaustive list; any random combination of any two of the above 43 markers falls within the scope of this application, and these combinations are explicit.

[0071] In some embodiments, the said markers are a combination of 3, such as: C7+PIGR+LGALS3BP、LGALS3BP+PIGR+LGALS3BP、VCAM1+PIGR+LGALS3BP、VWF+PIGR+LGALS3BP 、ALCAM+PIGR+LGALS3BP、TGFBI+PIGR+LGALS3BP、CNDP1+PIGR+LGALS3BP、IGFALS+PIGR+LGALS3BP、IGJ+PIGR+LGALS3BP、 EFEMP1+PIGR、A2M+PIGR、FCGBP+PIGR、SHBG+PIGR+LGALS3BP、COLEC11+PIGR+LGALS3BP、IL1RAP+PIGR+LGALS3BP、IGFBP3 +PIGR+LGALS3BP、FAP+PIGR+LGALS3BP、PVR+PIGR+LGALS3BP、ANPEP+PIGR+LGALS3BP、PON3+PIGR+LGALS3BP、FBLN1+PIGR+ LGALS3BP, FCGR3B+PIGR, LYVE1+PIGR+LGALS3BP, C3+PIGR+LGALS3BP, SERPINA1+PIGR+LGALS3BP, IGHM+PIGR+LGALS3BP, C1QA+PIGR+LGALS3BP, ADIPOQ+PIGR+LGALS3BP, A30+PIGR+LGALS3BP, LEPR+PIGR+LGALS3BP, CAT+PIGR+LGALS3BP, APOC1 +PIGR+LGALS3BP, DSG1+PIGR+LGALS3BP, CRP+PIGR+LGALS3BP, S100A8+PIGR+LGALS3BP, CDH1+PIGR+LGALS3BP, CSF1R+PIGR, IGHV5-51+PIGR, IGHV1-24+PIGR+LGALS3BP, SERPINA11+PIGR+LGALS3BP, MET+PIGR+LGALS3BP or CD5L+PIGR+LGALS3BP;Another example: PIGR+C7+LGALS3BP, LGALS3BP+C7+LGALS3BP, VCAM1+C7+LGALS3BP, VWF+C7+LGALS3BP, ALCAM+C7+LGALS3BP, TGFBI+C7+LGALS3BP, CNDP1+C7+LGALS3BP, IGFALS+C7+LGALS3BP, IGJ+C7+LGALS3BP, EFEMP1+C7+LGALS3BP, A2M+ C7+LGALS3BP, FCGBP+C7+LGALS3BP, SHBG+C7+LGALS3BP, COLEC11+C7+LGALS3BP, IL1RAP+C7+LGALS3BP, IGFBP3+C 7+LGALS3BP, FAP+C7+LGALS3BP, PVR+C7+LGALS3BP, ANPEP+C7+LGALS3BP, PON3+C7+LGALS3BP, FBLN1+C7+LGALS3BP , FCGR3B+C7+LGALS3BP, LYVE1+C7+LGALS3BP, C3+C7+LGALS3BP, SERPINA1+C7+LGALS3BP, IGHM+C7+LGALS3BP, C1Q A+C7+LGALS3BP, ADIPOQ+C7+LGALS3BP, A30+C7+LGALS3BP, LEPR+C7+LGALS3BP, CAT+C7+LGALS3BP, APOC1+C7+LGAL S3BP, DSG1+C7+LGALS3BP, CRP+C7+LGALS3BP, S100A8+C7+LGALS3BP, CDH1+C7+LGALS3BP, CSF1R+C7+LGALS3BP, IGHV5-51+C7+LGALS3BP, IGHV1-24+C7+LGALS3BP, SERPINA11+C7+LGALS3BP, MET+C7+LGALS3BP, or CD5L+C7+LGALS3BP. This is not an exhaustive list; any random combination of three of the above 43 markers falls within the scope of this application, and these combinations are explicit.

[0072] In some aspects, the markers are combinations of four, and any random combination of four markers out of the aforementioned 43 markers falls within the scope of this application; in some embodiments, the markers are combinations of five, and any random combination of five markers out of the aforementioned 43 markers falls within the scope of this application; in some aspects, the markers are combinations of six, and any random combination of six markers out of the aforementioned 43 markers falls within the scope of this application; in some embodiments, the markers are combinations of seven, and any random combination of seven markers out of the aforementioned 43 markers falls within the scope of this application; in some embodiments, the markers are combinations of eight, and any random combination of eight markers out of the aforementioned 43 markers falls within the scope of this application. Although there are many such combinations, they are all clearly defined and will not be presented one by one herein.

[0073] As used in this article, "hepatic fibrosis" or "HF" refers to the diffuse, excessive deposition and abnormal distribution of extracellular matrix components such as collagen, glycoproteins, and proteoglycans in the liver during the development of chronic liver disease. This is a pathological repair response of the liver to chronic damage and a crucial step in the progression of various chronic liver diseases to cirrhosis, significantly influencing the prognosis of chronic liver disease. Based on the degree of fibrosis, liver fibrosis is typically classified into five grades: F0 represents no liver fibrosis, F1 represents mild liver fibrosis, F2 represents moderate liver fibrosis, F3 represents severe liver fibrosis, and F4 represents cirrhosis. Existing "liver fibrosis" exists in a variety of diseases, including but not limited to: viral hepatitis, fatty liver (including non-alcoholic fatty liver and alcoholic liver disease), autoimmune diseases, and hepatic encephalopathy. Taking non-alcoholic fatty liver as an example, this disease is metabolic stress-induced liver damage closely related to insulin resistance and genetic susceptibility. The disease spectrum includes simple fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and NASH-related liver fibrosis and cirrhosis stages.

[0074] As used herein, the term "diagnosis" generally refers to the process of identifying a medical condition or disease (such as liver fibrosis) through its signs, symptoms, and especially the results of various diagnostic procedures (including detecting the levels of biomarkers in biological samples (such as serum) obtained from an individual). Furthermore, the term "diagnosis" as used herein includes early identification (or early warning), early screening (or early diagnosis), auxiliary diagnosis (or auxiliary diagnostic), definitive diagnosis (determining the presence or absence of the condition), and severity assessment of liver fibrosis. Without limitation, in some embodiments of this application, it has been sufficiently demonstrated that the levels of these biomarkers can be used to determine the presence or absence of liver fibrosis in a subject, and thus for applications such as early diagnosis, auxiliary diagnosis, definitive diagnosis, and severity assessment of liver fibrosis. In some specific embodiments of this application, the early diagnosis is achieved, in particular, by distinguishing between no significant liver fibrosis (corresponding to F0-F1 grade) and significant liver fibrosis (corresponding to F2-F4 grade); in other specific embodiments of this application, the determination of severity refers to the stratification or grading of the severity of liver fibrosis; the stratification or grading is particularly used to distinguish the severity of different grades of liver fibrosis; preferably, the severity includes no significant liver fibrosis (corresponding to F0-F1 grade) and significant liver fibrosis (corresponding to F2-F4 grade); it also includes different grades (F2-F4 grade) of significant liver fibrosis.

[0075] As used herein, “treatment” includes any treatment of a disease or condition in mammals, and includes: (a) preventing the disease from occurring in subjects who may be susceptible to the disease but have not yet been diagnosed with it; (b) suppressing the disease, i.e., preventing its development; or (c) alleviating the disease, i.e., causing partial or complete improvement of the disease. Therapeutic agents may be administered before, during, or after the onset of a disease or injury.

[0076] The terms “individual,” “subject,” “subject,” and “patient” are used interchangeably in this document and refer to any mammalian subject requiring diagnosis, treatment, or therapy, particularly humans, especially the Chinese population.

[0077] As used herein, the terms “sample,” “sample,” “test sample,” “subject sample,” “subject sample,” etc., encompass a wide range of sample types obtained from patients, individuals, or subjects and that may be used for diagnostic or monitoring assays. Patient samples may be obtained from healthy subjects, patients with illnesses, or patients with symptoms related to liver fibrosis. Furthermore, samples obtained from patients may be aliquoted, and only a portion may be used for diagnostic purposes. Additionally, samples or portions thereof may be stored under conditions that allow for subsequent analysis. This definition specifically includes blood and other liquid samples of biological origin (including, but not limited to, tissues, cells (e.g., PMBC cells, T cells, etc.), body fluids, serum, plasma, whole blood, urine, semen, saliva, pleural effusion, ascites, cerebrospinal fluid, feces, and synovial fluid, etc.). In one specific embodiment, the sample includes a blood sample. In another embodiment, a serum sample is used. This definition also includes samples that have been manipulated in any way after acquisition, such as by centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washing, or enrichment of certain cell populations. These terms also include clinical samples and further include cells in cultures, cell supernatants, tissue samples, organs, etc. Samples may also include freshly frozen and / or formalin-fixed, paraffin-embedded tissue blocks, such as blocks prepared from clinical or pathological biopsies, for pathological analysis or research by immunohistochemistry. Samples can be tested immediately after collection, stored at RT, 4°C, -20°C, or -80°C, and tested after 24 hours, 1 week, 1 month, 1 year, 10 years, or up to 30 years of storage.

[0078] This application demonstrates the sensitivity and specificity of biomarkers using the term "ROC curve," where "ROC" or "ROC curve" can refer to a receptor operating characteristic curve. An ROC curve can be a graphical representation of the performance of a binary classifier system. For any given method, an ROC curve can be generated by plotting sensitivity against specificity at various threshold settings. Furthermore, provided at least one of three parameters (e.g., sensitivity, specificity, and threshold setting) is provided, the ROC curve can determine the value or expected value of any unknown parameter. Unknown parameters can be determined using a curve fitted to the ROC curve.

[0079] The diagnostic performance evaluation section of this application is based on AUC curve evaluation. As used herein, the term “AUC” or “ROC-AUC” generally refers to the area under the receptor operating characteristic curve. This metric takes into account the sensitivity and specificity of the method and provides a measure of the method’s diagnostic utility. Typically, ROC-AUC is in the range of 0.5 to 1.0, where values ​​closer to 0.5 indicate limited diagnostic utility (e.g., low sensitivity and / or specificity), and values ​​closer to 1.0 indicate greater diagnostic utility (e.g., high sensitivity and / or specificity). See, for example, Pepe et al., “Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker”, Am.J. Epidemiol 2004, 159(9):882-890, which is incorporated herein by reference in its entirety. Other methods for characterizing diagnostic utility using likelihood functions, odds ratios, information theory, predicted values, calibration (including goodness of fit), and reclassification measures are outlined in Cook, “Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction”, Cir Calculation 2007, 115:928-935, which is incorporated herein by reference in its entirety.

[0080] As used in this article, "statistically significant" refers to the probability that the relationship between two or more variables is caused by factors other than random chance. Statistical hypothesis testing is used to determine whether the results of a dataset are statistically significant. In statistical hypothesis testing, a statistically significant result is obtained as long as the p-value of the observed test statistic is less than the significance level defined by the study. The p-value is the probability of obtaining a result at least as extreme as the observed result when the null hypothesis is true. Examples of statistical hypothesis analysis include the Wilcoxon signed-rank test, the t-test, and chi-square or Fisher's exact test.

[0081] Although this application has identified a series of biomarkers with excellent diagnostic performance for liver fibrosis, in order to further improve diagnostic performance, other known or existing biomarkers, or even some clinical indicators, can be combined with the biomarkers in this application, such as SOFA score (oxygenation index PaO2 / FiO2, platelet count, bilirubin concentration, mean arterial pressure, creatinine, urine output), white blood cell count (WBC), erythrocyte sedimentation rate (ESR), etiological examination, neutrophil gelatinase-associated lipocalin (NGAL), pancreatic amylase, pancreatic lipase, lactate dehydrogenase (LDH), alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), lactate, potassium, sodium, chlorine, blood glucose, or one or more of blood urea nitrogen, etc., without limitation.

[0082] In this application, the detection reagent or component for obtaining the level of a biomarker in a sample can be understood to include a detection reagent that directly obtains the level of a biomarker in a sample, or a component that indirectly obtains the level of a biomarker in a sample (such as a computer program that directly obtains the level of a biomarker in a sample that has already been tested).

[0083] Protein levels can be obtained at either the nucleic acid level or the protein level. No specific detection method is limited; any method that can be used to directly or indirectly evaluate the protein level of the biomarker is suitable for this application.

[0084] It is understood that the nucleic acid level may include the DNA level or the RNA level. This application specifically targets the detection of nucleic acids at the transcriptional level, which can reflect the protein level. There are various methods in the art for detecting specific nucleic acid levels, including but not limited to sequencing technology, nucleic acid amplification technology, nucleic acid hybridization technology, electrophoresis technology, biomolecular mass spectrometry, or chromatography technology. All of these technologies can be used in this application. In some specific embodiments of this application, including but not limited to any of the following specific methods: gene sequencing, polymerase chain reaction, isothermal amplification reaction, gene chip method, probe hybridization, gel electrophoresis, RNA blotting, nucleic acid mass spectrometry, or liquid chromatography.

[0085] For example, in some embodiments, the gene sequencing method can be transcriptome sequencing or genome sequencing. Genome sequencing can employ first-generation Sanger sequencing, second-generation sequencing (“NGS”), or third-generation sequencing reagents. Second-generation sequencing (“NGS”) differs from “Sanger sequencing” (first-generation sequencing), which is based on the electrophoretic separation of chain termination products in a single sequencing reaction. NGS is a revolutionary change from traditional Sanger sequencing technology, capable of sequencing hundreds of thousands to millions of nucleic acid molecules at once. The sequencing platforms for NGS used in this application are commercially available, including but not limited to Roche / 454FLX, Illumina / Solexa GenomeAnalyzer, and Applied Biosystems SOLID system. Transcriptome sequencing can also rapidly and comprehensively obtain almost all transcripts and gene sequences of a specific cell or tissue of a species in a specific state through a second-generation sequencing platform, which can be used to study gene expression levels, gene function, structure, alternative splicing, and new transcript prediction. In some other embodiments of this application, the nucleic acid sequencing method can be single-molecule real-time sequencing. Single-molecule DNA sequencing technology is a new generation sequencing technology that has been developed in the last 10 years, also known as third-generation sequencing technology, including single-molecule real-time sequencing, true single-molecule sequencing, single-molecule nanopore sequencing and other technologies.

[0086] It is understood that there are various protein detection methods in the art, including but not limited to sequencing technology, immunoassay technology, electrophoresis technology, biomolecular mass spectrometry technology, or chromatography technology, all of which can be used in this application; in some specific embodiments of this application, including but not limited to any of the following specific methods: amino acid sequencing, enzyme-linked immunosorbent assay (ELISA), chemiluminescence, immunochromatography, radioimmunoassay, immunohistochemistry, immunoblotting, flow cytometry, gel electrophoresis, adjacent extension analysis (PEA), SomaScan based on nucleic acid aptamers, proteometry, or liquid chromatography.

[0087] For example, in some embodiments, methods for quantifying a protein biomarker, such as a biomarker, include specific antibodies. Specific antibodies are antibodies capable of specifically recognizing the biomarker protein, including but not limited to monoclonal antibodies (e.g., full-length or intact monoclonal antibodies), polyclonal antibodies, multivalent antibodies, multispecific antibodies (e.g., bispecific antibodies with the intended biological activity), nanobodies, or at least one of certain antibody fragments. In some specific embodiments, measuring the level of a protein biomarker (e.g., C7) includes contacting a sample with a first specific binding member and a second specific binding member. In some embodiments, the first specific binding member is a capture antibody, and the second specific binding member is a detection antibody. In some embodiments, measuring C7 levels includes contacting a sample simultaneously or in any order with: (1) a capture antibody (e.g., a C7 capture antibody) that binds to an epitope on C7 or a C7 fragment to form a capture antibody-C7 antigen complex (e.g., a C7 capture antibody-C7 antigen complex), and (2) a detection antibody (e.g., a C7 detection antibody) that contains a detectable marker and binds to an epitope on C7 that is not bound by the capture antibody to form a C7 antigen-detection antibody complex (e.g., a C7 antigen-C7 detection antibody complex), thereby forming a capture antibody-C7 antigen-detection antibody complex (e.g., a C7-capture antibody-C7 antigen-C7 detection antibody complex), and measuring the amount or concentration of C7 in the sample based on a signal generated by the detectable marker in the capture antibody-C7 antigen-detection antibody complex.

[0088] In some embodiments of this application, the test sample described in this application may be selected from tissues, cells, body fluids, serum, plasma, whole blood (peripheral blood), urine, semen, saliva, pleural effusion, ascites, cerebrospinal fluid, feces, and synovial fluid; in preferred embodiments, the test sample is selected from any one of tissues, cells, serum, plasma, whole blood, urine, semen, and saliva.

[0089] The diagnostic method of this application

[0090] The applicant discovered that the selected biomarkers or combinations of biomarkers could be used for the diagnosis of liver fibrosis, and therefore provided a specific diagnostic method for liver fibrosis.

[0091] This method generally includes the steps of acquiring the aforementioned biomarkers from subject samples, and in some embodiments, it includes the following steps:

[0092] (i) Obtain the levels of the biomarkers described above in the subject samples;

[0093] (ii) Comparison with the corresponding biomarker levels in the control sample; wherein a significant difference in the biomarker levels between the subject sample and the control sample is an indication that the subject has or will have liver fibrosis; or

[0094] (ii) Compare with a set absolute threshold; wherein, a level of the biomarker in the subject sample that is higher than the absolute threshold is an indication that the subject has or will have liver fibrosis.

[0095] It is understood that the type of control sample can be selected according to the actual diagnostic needs or the actual application scenario. For example, in the early diagnosis of liver fibrosis, the control sample is a sample from a normal population or a sample from a population whose liver fibrosis is graded as F0 or F1.

[0096] In some specific embodiments, a set value for the biomarker level can also be provided. This set value can be determined based on the biomarker levels of normal samples and / or normal samples from patients without liver fibrosis. For example, the average expression level of the biomarker in a suitable number of normal samples can be selected, or a reasonable multiple can be set based on this average value, such as 0.9 times, 0.8 times, 0.7 times, 0.6 times, 0.5 times, etc. When the biomarker level of a subject is higher than this set value, liver fibrosis is judged. It is understood that the set value determined based on the average value or a multiple of the average value needs to have good classification significance. Common statistical tests can be used to test known samples based on the classification of the set value. When the results are statistically significant, it indicates that the set value can be used as a judgment criterion.

[0097] Obtaining biomarker levels refers to the values ​​of biomarkers for a subject, derived directly or indirectly from direct measurements, and are typically at least partially derived from the amount of the biomarker in the subject's sample. Indirectly derived values ​​include those obtained by applying a function to the measured value of the biomarker.

[0098] The treatment method of this application

[0099] The treatment method described in this application is a treatment method for liver fibrosis, including the steps of any of the aforementioned diagnostic methods, and further including the step of administering appropriate medication to the subject based on the diagnostic results.

[0100] As used herein, “treatment” includes any treatment of a disease or condition in mammals, and includes: (a) preventing the occurrence of a disease in subjects who may be susceptible to the disease but have not yet been diagnosed with it; (b) suppressing a disease, i.e., halting its development; or (c) alleviating a disease, i.e., causing partial or complete improvement in the disease. Therapeutic agents may be administered before, during, or after the onset of a disease or injury. This application specifically refers to the treatment of liver fibrosis.

[0101] It is understood that treatment regimens for different degrees of liver fibrosis are different and known. The diagnostic method of this application can achieve purposes such as early diagnosis and severity stratification, and accordingly different dosing regimens can be selected for the treatment of liver fibrosis.

[0102] The product of this application

[0103] Based on the core purpose of this biomarker, procedures for detecting biomarker levels can be configured into corresponding product forms for the diagnosis of liver fibrosis. These product forms include reagents or components for detecting the biomarker or combinations of biomarkers. It is understood that such product forms can be varied, including but not limited to: kit forms, system devices, computer-readable media, or computer systems.

[0104] reagent kit form

[0105] In some embodiments of this application, kits for detecting or analyzing biomarker levels to diagnose liver fibrosis are also disclosed. Such kits may include reagents for detecting the levels of the aforementioned biomarkers and instructions for diagnosing liver fibrosis based on the detected levels.

[0106] The kit may include a set of reagents that are used to quantify the level of a biomarker by at least one assay.

[0107] As described above, when the detection reagent is used for detection at the nucleic acid level (especially the transcriptional level), the detection can be achieved through sequencing technology, nucleic acid amplification technology, nucleic acid hybridization technology, electrophoresis technology, biomolecular mass spectrometry technology, or chromatography technology; for example, including but not limited to any of the following specific methods: gene sequencing, polymerase chain reaction, isothermal amplification reaction, gene chip method, probe hybridization method, gel electrophoresis, RNA blotting, nucleic acid mass spectrometry, or liquid chromatography. Therefore, the main components of the corresponding kit can be sequencing reagents, PCR primers, probes, mass spectrometry reagents, etc.

[0108] For example, in some specific embodiments, the reagent can be a sequencing reagent, such as first-generation Sanger sequencing reagent, second-generation sequencing (“NGS”) reagent, third-generation sequencing reagent, etc. In other specific embodiments, the reagent is a PCR primer reagent, wherein the primer refers to a primer capable of specifically amplifying the C7 gene, such as a polynucleotide of a certain length, such as a primer of about 35 nucleotides or longer, which can hybridize with at least a portion of the template sequence and serve as the starting site for synthesizing primer extension products. In other specific embodiments, the reagent is a probe reagent, wherein the probe refers to a probe capable of specifically recognizing the C7 gene or its transcript, which is a molecule capable of binding to a specific sequence or subsequence or other part of another molecule, typically referring to a nucleic acid probe that binds to another nucleic acid (i.e., the specific sequence of C7 as the target nucleotide) through complementary base pairing.

[0109] When the detection reagent is used to detect proteins, as mentioned above, there are various protein detection methods, including but not limited to sequencing, immunoassay, electrophoresis, mass spectrometry, or chromatography. These methods may include, but are not limited to, any of the following specific methods: amino acid sequencing, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, immunohistochemistry, Western blotting, flow cytometry, gel electrophoresis, adjacent extension analysis (PEA), SomaScan based on nucleic acid aptamers, proteometry, or liquid chromatography. Therefore, the main components of the kit can be protein sequencing reagents, antibody components, etc.

[0110] For example, in some specific embodiments, the reagent is a specific antibody, wherein a specific antibody is an antibody capable of specifically recognizing a protein in a biomarker, specifically including but not limited to monoclonal antibodies (e.g., full-length or intact monoclonal antibodies), polyclonal antibodies, multivalent antibodies, multispecific antibodies (e.g., bispecific antibodies with the intended biological activity), or at least one comprising certain antibody fragments. In some specific embodiments, measuring the protein level includes contacting the sample with a first specific binding member and a second specific binding member. In some embodiments, the first specific binding member is a capture antibody, and the second specific binding member is a detection antibody. In some embodiments, taking the C7 protein as an example, measuring the C7 level includes contacting the sample simultaneously or in any order with: (1) a capture antibody (e.g., a C7 capture antibody) that binds to an epitope on C7 or a C7 fragment to form a capture antibody-C7 antigen complex (e.g., a C7 capture antibody-C7 antigen complex), and (2) a detection antibody (e.g., a C7 detection antibody) that contains a detectable marker and binds to an epitope on C7 that is not bound by the capture antibody to form a C7 antigen-detection antibody complex (e.g., a C7 antigen-C7 detection antibody complex), thereby forming a capture antibody-C7 antigen-detection antibody complex (e.g., a C7-capture antibody-C7 antigen-C7 detection antibody complex), and measuring the amount or concentration of C7 in the sample based on a signal generated by the detectable marker in the capture antibody-C7 antigen-detection antibody complex.

[0111] In some embodiments, such a kit may include a carrier, packaging, or container compartmentalized to receive one or more containers, such as vials, tubes, etc., each containing one of the independent elements to be used in the method. The kit of this application may include the containers described above, as well as one or more other containers containing substances required from a commercial end-user perspective, including buffers, diluents, filters, and packaging instructions with usage instructions.

[0112] In some embodiments, the kit further includes sample processing reagents, which may include at least one of sample lysis reagents, sample purification reagents, and sample extraction reagents.

[0113] In some embodiments, the product further includes at least one of standards, calibrators, control standards, and buffer solutions. For example, a control standard is a control used to verify the validity of the experiment and serves as a reference for judging the results. A buffer solution can be any solution known in the art that can provide appropriate buffering conditions during the detection process.

[0114] The kit may include instructions for use of a set of reagents. For example, the kit may include instructions for performing at least one assay, such as immunoassays, protein binding assays, antibody-based assays, antigen-binding protein-based assays, protein arrays, enzyme-linked immunosorbent assays (ELISA), flow cytometry, protein arrays, Western blotting, protein blotting, turbidimetry, chromatography, mass spectrometry, enzyme activity assays, and immunoassays, wherein the immunoassays are selected from RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, immunoelectrophoresis, competitive immunoassays, and immunoprecipitation.

[0115] In addition to the components described above, the kit will further include instructions for implementing the subject method. These instructions may exist in various forms within the subject kit, including one or more forms. One possible form of these instructions is as printed information on a suitable medium or substrate, such as one or more sheets of paper with the information printed thereon, which are included in the kit packaging as inserts, etc.

[0116] System device

[0117] In some embodiments of this application, systems for detecting or analyzing biomarker levels to diagnose liver fibrosis are also disclosed herein. Such a system may include a set of reagents for detecting the levels of one or more biomarkers; a device configured to receive a mixture of one or more reagents and a test sample obtained from a subject to measure the biomarker levels; and a computer system communicatively coupled to the device to obtain the measured levels and determine a score predicting the likelihood of liver fibrosis in a patient with liver fibrosis.

[0118] The device is configured to determine the biomarker level in a mixture of a detection reagent and a test sample. For example, the device can determine the biomarker level using an immunoassay or an assay for nucleic acid detection. The mixture of reagents and test samples can be supplied to the device in various containers; examples of such containers, for protein detection, include wells of a plate (e.g., a 96-well plate), vials, or tubes. Therefore, the device can have openings (e.g., slots, cavities, openings, sliding trays) that can receive and read the container holding the reagent-test-sample mixture to generate a quantitative expression value for a soluble medium. Examples of devices include microplate readers (e.g., luminescent microplate readers, absorbance microplate readers, fluorescence microplate readers), spectrometers, and spectrophotometers.

[0119] The computer system communicates with the device to receive quantitative expression values ​​of the soluble media. The computer system analyzes the quantitative expression values ​​by applying a predictive model and determines the likelihood of liver fibrosis activity events in the subjects.

[0120] Computer system or computer-readable medium

[0121] In some embodiments of this application, the methods or uses of this application may be performed using a computer, including detecting or analyzing C7 levels by computer to diagnose liver fibrosis.

[0122] For example, the construction and execution of a predictive model for generating a score (e.g., an LFPI score) can be implemented in hardware, software, or a combination of both. In one embodiment, a readable storage medium is provided, such as a medium comprising data storage material encoded with machine-readable data, which, when used with a machine programmed to utilize the data, is capable of displaying any dataset of the predictive model of this application, as well as execution and results. Such data can be used for a variety of purposes, such as monitoring, diagnosis, and treatment considerations for patients with liver fibrosis. Embodiments of the above methods can be implemented in a computer program that executes on a programmable computer, including a processor, a data storage system, a graphics adapter, a network adapter, at least one input device, and at least one output device. A display is coupled to the graphics adapter. Program code is applied to the input data to perform the above functions and generate output information. The output information is applied to one or more output devices in a known manner. The computer can be, for example, a personal computer, a microcomputer, or a conventionally designed workstation.

[0123] Each program can be implemented in a high-level program or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled language or an interpreted language. Each such computer program is preferably stored on a general-purpose or special-purpose programmable computer-readable storage medium or device (e.g., ROM or disk) for configuring and operating the computer to execute the program described herein when the computer reads the storage medium or device. The system can also be considered as being implemented in the form of a computer-readable storage medium configured with computer programs, wherein such a storage medium causes the computer to operate in a specific and predefined manner to perform the functions described herein.

[0124] Feature patterns and their databases can be provided in various media to facilitate their use. "Media" refers to an article of manufacture containing the feature pattern information of this application. The database of this application can be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tape; optical storage media, such as CD-ROMs; electronic storage media such as RAM and ROM; and mixtures of these categories, such as magnetic / optical storage media. Those skilled in the art will readily understand how any currently known computer-readable medium can be used to produce an article of manufacture containing records containing information from the current database. "Record" refers to the process of storing information on a computer-readable medium using any such method known in the art. Any convenient data storage structure can be chosen based on the method used to access the stored information. Storage can be performed using various data processing programs and formats, such as word processing text files, database formats, etc.

[0125] Other aspects

[0126] It is understood that, based on the conviction that the biomarker of this application can be used to diagnose liver fibrosis and improve its performance, those skilled in the art can use this information to evaluate or screen other potential diagnostic biomarkers for liver fibrosis. Typically, by evaluating the correlation between potential biomarkers and the biomarker of this application, and by drawing a positive correlation conclusion, it is helpful to explain the value of the potential diagnostic biomarker.

[0127] This application may also relate to a method for detecting biomarkers in subjects who have or are suspected of having liver fibrosis in vivo or in vitro, the method comprising determining or detecting the level of the biomarker in a biological sample from the subject, the method may include non-disease diagnostic uses.

[0128] In addition, this application may also relate to a method for screening liver fibrosis treatment agents in vivo or in vitro, the method including the step of evaluating biomarker levels in samples treated with the treatment agent, and evaluating the therapeutic effect of the treatment agent based on the biomarker levels, for example, determining the therapeutic effect of the treatment agent when the biomarker level decreases.

[0129] The implementation scheme of this application will be described in detail below with reference to the embodiments.

[0130] Example 1: Differential Protein Mining Based on Proteomics

[0131] This application is based on the appendix as a whole. Figure 1 The process involves the discovery and performance validation of protein biomarkers in liver fibrosis tissue, and some specific steps include the following aspects.

[0132] I. Sample Collection

[0133] To screen for biomarkers that can diagnose liver fibrosis, the applicant collected multiple liver fibrosis samples of different grades from January 2020 to October 2020, including 10 patients with grade 4 liver fibrosis, 10 patients with grade 3 liver fibrosis, 10 patients with grade 2 liver fibrosis, 10 patients with grade 1 liver fibrosis, and 8 patients with grade 0 liver fibrosis and non-alcoholic fatty liver disease.

[0134] At enrollment, patients with alcoholic fatty liver disease, viral hepatitis, and drug-induced liver injury were excluded. All enrolled patients signed informed consent forms. The degree of liver fibrosis in all patients was confirmed by histopathological examination of liver biopsy samples. After obtaining informed consent, whole blood samples were collected from patients using EDTA-containing anticoagulant tubes. Detailed clinical information, including the collection date, hospital number, gender, age, and pathology report, was recorded. The samples were centrifuged at 3000×g for 15 minutes at 4°C within 4 hours, and the supernatant pale yellow plasma was carefully aspirated in a biosafety cabinet, aliquoted into sterile 1.5ml centrifuge tubes, and stored at -80°C for later use.

[0135] II. Sample Preprocessing

[0136] Plasma samples were centrifuged for 15 minutes (15000×g) to remove cell debris, and the supernatant was transferred to a new 1.5 ml centrifuge tube. The supernatant was then concentrated using a 3 kDa cutoff column at 4000×g for 1 hour. The concentrate was recovered and buffer exchanged using a 7 kDa cutoff desalting column at 1000×g for 2 minutes. The replacement buffer was AEX-A (20 mM Tris, 4 M Urea, 3% isopropanol, pH 8.0). Using AEX-A as a blank, protein concentration in the sample was determined using the BCA method (protein concentration assay). TCEP (Thermo Scientific, CAT#77720) was then added to the sample, and the sample was incubated at 37°C for 30 minutes for protein reduction. Subsequently, the sample was buffer exchanged using a Zeba column (Thermo Scientific, CAT#89890). Samples were filtered using a 0.22 μm filter and pre-separated using a 2D-HPLC system. The collected fractions were then lyophilized for later use. Simultaneously, a fresh trypsin / LysC mixture (Thermo Scientific, CAT#A41007) was prepared at a ratio of 1:100 (enzyme to total protein) of 20 μL per sample. 20 μL of the trypsin / LysC mixture (0.05 μg / μL) was added to each centrifuge tube, bringing the final volume to 40 μL. The samples were then incubated at 37°C for 5 hours to digest the enzymes, followed by the addition of 5 μL of 10% trifluoroacetic acid to terminate the digestion. The HPLC fractions were then analyzed by LC-MS / MS.

[0137] III. Data Acquisition by Liquid Chromatography-Mass Spectrometry (LC-MS / MS)

[0138] The LC-MS / MS system used was an Easy-nLC 1200 (Thermo Scientific) coupled with a Q Exactive HFX (Thermo Scientific). Mobile phase A was an aqueous solution containing 0.1% formic acid and 2% acetonitrile; mobile phase B was an aqueous solution containing 0.1% formic acid and 90% acetonitrile. The analytical column packing material was Dr. Maisch GmbH's ReproSil-Pur C18-AQ, with 1.9 μm resin particles. 1 μg peptide fragments were dissolved in mobile phase A and then separated using the EASY-nLC 1200 ultra-high performance liquid chromatography system. The chromatographic gradient conditions were set as follows: 0–30 min, 10%–22% mobile phase B; 31–53 min, 22%–32% mobile phase B; 54–57 min, 32%–80% mobile phase B; 58–60 min, 80%–100% mobile phase B. The liquid flow rate was maintained at 450 nL / min.

[0139] After HPLC separation, the peptide fragments were injected into a NanoFlex ion source, nebulized, and then introduced into a Q Exactive HF-X combined quadrupole Orbitrap. TM Mass spectrometry analysis was performed using a mass spectrometer. The ion source voltage was set to 2.1 kV, the primary mass spectrometry scan range was set to 300-1650 m / z, and the resolution was set to 120,000; the secondary mass spectrometry scan range start point was set to 100 m / z, and the resolution was set to 15,000. In data-dependent scanning (DDA) mode, the top 15 precursor ions were sequentially entered into the high-energy collision dissociation (HCD) cell for fragmentation, followed by sequential secondary mass spectrometry analysis. Automatic gain control (AGC) was set to 5 × 10⁻⁶. 4 The signal threshold is set to 1×10. 4 The maximum injection time was set to 25 ms. To avoid repeated scanning of high-abundance peptides, the dynamic exclusion time for tandem mass spectrometry analysis was set to 30 seconds.

[0140] IV. Data Acquisition and Processing (Protein Identification and Quantification)

[0141] The mass spectrometry files (raw format) obtained by LC-MS / MS were processed sequentially using quantMS software as follows: (1) converting the raw format file to mzML format (thermorawfileparser), (2) peptide identification (comet and msgf+), (3) scoring the peptide identification results (percolator), (4) calculating the FDR of the peptide identification results (openms fdr tool), (5) protein modification localization (luciphor), (6) peptide quantification (proteomicsLFQ), (7) protein inference and quantification (proteomicsLFQ), (8) QC report generation (pmultiqc), and (9) normalization and missing value imputation (MSstats). Specifically, the data type was proteomics data based on secondary reporter ion quantification, and the secondary spectra used for quantification required the parent ion to account for more than 75% of the primary spectra. The search database was the Uniprot database's Homo_sapiens_9606_proteome (downloaded May 30, 2024, SWISS-Prot protein sequence database, containing 20,428 protein sequences). Enzyme digestion was set to Trypsin / P; the maximum allowed number of missed cleavage sites was set to 2; the tolerance for precursor ion mass error in First Search and Main Search was set to 20 ppm and 5 ppm, respectively, and the tolerance for secondary fragment ion mass error was 20 ppm. The fixed modification was cysteine ​​alkylation, and the variable modifications were methionine oxidation and N-terminal acetylation of the protein. The FDR for both PSM identification and protein identification was set to 1%.

[0142] V. Screening of Differentially Expressed Proteins

[0143] Differential proteins in the plasma of patients with liver fibrosis grades 0-1 and 2-4 were screened using a combination of univariate and multivariate statistical analyses. Univariate analysis primarily included significance analysis and fold change analysis of identified proteins in different groups, while multivariate statistical analysis mainly included principal component analysis and unsupervised hierarchical cluster analysis. Unsupervised principal component analysis and hierarchical cluster analysis were used to analyze the separation trends of proteins between groups. The applicant identified and quantified 510 proteins in the patient plasma. Of these 510 proteins, 67 proteins showed significant differences in expression levels (P < 0.05 and fold change ≥ 1.2) (see [link to relevant documentation]). Figure 2-3 (and Table 1).

[0144] In addition, ROC curves for the diagnostic performance of each individual biomarker were established, and the area under the curve (AUC) was used to determine the diagnostic performance. An AUC of 0.5 indicates that the individual protein has no diagnostic value; an AUC greater than 0.5 indicates that the individual protein has diagnostic value; and the larger the AUC, the higher the diagnostic value of the individual protein. Furthermore, ROC curve analysis of the individual biomarkers revealed that 45 out of 67 differentially expressed proteins had an AUC ≥ 0.7, indicating good clinical application value (Table 1).

[0145] Table 1. Clinically valuable differentially expressed proteins identified in the cohort.

[0146]

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[0148] Example 2: Validation of protein biomarkers and their combinations

[0149] The applicant independently validated the differentially expressed proteins and their combinations with diagnostic value identified in the discovery cohort using a separate cohort of liver fibrosis patients. Specifically, the validation cohort collected plasma samples from 158 patients, including 24 patients with grade 0 liver fibrosis, 30 with grade 1, 46 with grade 2, 33 with grade 3, and 25 with grade 4. The inclusion criteria for the validation cohort were the same as those for the discovery cohort. All patients signed informed consent forms.

[0150] The validation cohort also performed plasma proteomics analysis based on LC-MS / MS. The experimental procedures and data processing methods, such as protein identification and quantification, were consistent with those of the discovery cohort.

[0151] Considering that the diagnostic capabilities of a single protein biomarker often fall short of the clinical requirements for high sensitivity and specificity, the applicant, in addition to validating individual biomarkers, also utilized logistic regression to screen protein biomarker combinations, identifying some combinations with superior diagnostic performance. Subsequently, the applicant constructed liver fibrosis diagnostic models with different biomarker combinations in the training set (used to distinguish between patients without significant liver fibrosis (F0-F1 grades) and patients with significant liver fibrosis (F2-F4 grades)) and performed performance validation in the aforementioned independent validation cohort.

[0152] 1. Performance verification of single markers

[0153] Differential protein expression analysis in the validation cohort revealed that 43 of the 67 differentially expressed proteins identified in Example 1 were also significantly different in the independent validation cohort (P < 0.05 and fold change ≥ 1.2) (Table 2). ROC curve analysis showed that 35 of the 43 differentially expressed proteins had an AUC ≥ 0.7 in the independent validation cohort, indicating that they have good clinical application value (Table 2).

[0154] Table 2. Differentially expressed proteins that have been repeatedly validated in independent clinical cohorts.

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[0156]

[0157] 2. Performance verification of marker combinations

[0158] As described above, the applicant used logistic regression algorithm to jointly model different biomarker combinations in the training set, constructing diagnostic models F0-F1 and F2-F4, and then evaluated the diagnostic performance of different biomarker combinations in the aforementioned independent clinical validation cohorts.

[0159] The applicant paired off 43 individual biomarkers (Table 2, 43 in total) that demonstrated diagnostic performance in both the discovery and validation cohorts (903 combinations in total). A joint biomarker model was constructed in the discovery cohort using logistic regression, and then used to predict the validation cohort, evaluating the joint diagnostic efficacy of the biomarker combinations. Of the 903 combinations, 437 combinations had an AUC value exceeding 0.85 in the independent validation cohort (Table 3).

[0160] Furthermore, based on the same approach, the applicant grouped any three of the 43 biomarkers (a total of 12,341 combinations) and used logistic regression to construct a joint diagnostic model in the discovery queue, and then evaluated its diagnostic efficacy in the aforementioned independent validation queue. Of the 12,341 combinations, 333 combinations had an AUC exceeding 0.9 in the independent validation queue (Table 4).

[0161] Since the number of possible combinations of any four biomarkers reaches 123,410, and further increasing the number of biomarker combinations does not significantly increase diagnostic efficacy in independent cohorts, this application does not list the diagnostic performance of combinations of four or more biomarkers. However, those skilled in the art can easily derive combinations of four or more biomarkers based on the biomarkers listed in this application. Therefore, any combination of one, two, three, or more biomarkers derived from the biomarkers listed in this application should be considered within the scope of this application.

[0162] Table 3

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[0173] Table 4

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[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. The application of detection reagents or components for obtaining biomarker levels in samples in the preparation of diagnostic products for liver fibrosis, or the application of biomarkers in the diagnosis of liver fibrosis; The markers include: C7, PIGR, LGALS3BP, VCAM1, VWF, ALCAM, TGFBI, CNDP1, IGFALS, IGJ, EFEMP1, A2M, FCGBP, SHBG, COLEC11, IL1RAP, IGFBP3, FAP, PVR, ANPEP, PON3, FBLN1, FCGR3B, LYVE1, C3, SERPINA1, IGHM, C1QA, ADIPOQ, A30, LEPR, CAT, APOC1, DSG1, CRP, S100A8, CDH1, CSF1R, IGHV5-51, IGHV1-24, SERPINA11, MET and / or CD5L (any, multiple, or all).

2. A product for the diagnosis of liver fibrosis, characterized in that, Includes detection reagents or components for obtaining marker levels in samples; The markers include any one, multiple, or all of the following: C7, PIGR, LGALS3BP, VCAM1, VWF, ALCAM, TGFBI, CNDP1, IGFALS, IGJ, EFEMP1, A2M, FCGBP, SHBG, COLEC11, IL1RAP, IGFBP3, FAP, PVR, ANPEP, PON3, FBLN1, FCGR3B, LYVE1, C3, SERPINA1, IGHM, C1QA, ADIPOQ, A30, LEPR, CAT, APOC1, DSG1, CRP, S100A8, CDH1, CSF1R, IGHV5-51, IGHV1-24, SERPINA11, MET, and / or CD5L.

3. The application according to claim 1 or the product according to claim 2, characterized in that, The levels include nucleic acid levels and / or protein levels; Preferably, the nucleic acid level is obtained by sequencing technology, nucleic acid amplification technology, nucleic acid hybridization technology, electrophoresis technology, biomass spectrometry technology, or chromatography technology; preferably, the method for obtaining the nucleic acid level includes, but is not limited to, any one of the following: gene sequencing, polymerase chain reaction, isothermal amplification reaction, gene chip method, probe hybridization method, gel electrophoresis, RNA blotting, nucleic acid mass spectrometry, or liquid chromatography. Preferably, the protein level is obtained by sequencing technology, immunoassay technology, electrophoresis technology, biomolecular mass spectrometry technology, or chromatography technology; preferably, the protein level acquisition method includes, but is not limited to, any one of the following methods: amino acid sequencing, enzyme-linked immunosorbent assay (ELISA), chemiluminescence immunoassay, immunochromatography, radioimmunoassay, immunohistochemistry, Western blotting, flow cytometry, gel electrophoresis, adjacent extension analysis (PEA), SomaScan based on nucleic acid aptamers, proteometry, or liquid chromatography.

4. The application according to claim 1 or the product according to claim 2, characterized in that, The samples include, but are not limited to, one or more of the following: tissues, cells, body fluids, serum, plasma, whole blood, urine, semen, saliva, pleural effusion, ascites, cerebrospinal fluid, feces, or synovial fluid.

5. A biomarker for the diagnosis of liver fibrosis, characterized in that, The markers include any one, multiple, or all of the following: C7, PIGR, LGALS3BP, VCAM1, VWF, ALCAM, TGFBI, CNDP1, IGFALS, IGJ, EFEMP1, A2M, FCGBP, SHBG, COLEC11, IL1RAP, IGFBP3, FAP, PVR, ANPEP, PON3, FBLN1, FCGR3B, LYVE1, C3, SERPINA1, IGHM, C1QA, ADIPOQ, A30, LEPR, CAT, APOC1, DSG1, CRP, S100A8, CDH1, CSF1R, IGHV5-51, IGHV1-24, SERPINA11, MET, and / or CD5L.

6. A method for diagnosing liver fibrosis in vivo or in vitro, characterized in that, The method includes the step of obtaining the biomarkers as described in claim 5 from the subject sample; preferably, the method specifically includes the following steps: (i) Obtaining the levels of the biomarkers as described in claim 5 in the subject samples; (ii) Comparison with the corresponding biomarker levels in the control sample; wherein a significant difference in the biomarker levels between the subject sample and the control sample is an indication that the subject has or will have liver fibrosis; or (ii) Compare with a set absolute threshold; wherein, a level of the biomarker in the subject sample that is higher than the absolute threshold is an indication that the subject has or will have liver fibrosis.

7. A method for detecting the biomarker of claim 5 in vivo or in vitro in a subject suffering from or suspected of having liver fibrosis, characterized in that, The method includes identifying or detecting biomarkers as described in claim 5 from a subject sample.

8. A method for evaluating or screening diagnostic markers for liver fibrosis, characterized in that, It includes the step of performing a correlation or consistency analysis between the potential biomarker and the biomarker of claim 5.

9. A method for screening therapeutic agents for liver fibrosis in vivo or in vitro, characterized in that, The method includes the step of evaluating the level of the biomarker of claim 5 on a sample treated with the therapeutic agent.

10. A treatment method for liver fibrosis, characterized in that, The method includes the diagnostic method of claim 6, and further includes the step of administering medication to the subject based on the diagnostic results.

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