Biomarker for Budd-Chiari syndrome and application thereof

Abstract

The invention discloses a Budd-Chiari syndrome biomarker and application thereof. The Budd-Chiari syndrome biomarker comprises any one or a combination of any two or more of IGF1 (Insulin-like Growth Factor 1), SEPRIND1, SEPRINA4, VCAM1 and ICAM1. As a blood marker, the Budd-Chiari syndrome marker can be used for Budd-Chiari syndrome risk prediction, diagnosis, screening or prognosis evaluation product preparation, early discovery can be achieved, the Budd-Chiari syndrome diagnosis accuracy can be improved, and the missed diagnosis rate and the misdiagnosis rate of diseases can be reduced. The biomarker provided by the invention is obtained based on serum, and is expected to realize identification and screening of people with Budd-Chiari syndrome in large-scale application.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a Budd-Chiari syndrome biomarker and application thereof. Background Art

[0002] Budd-Chiari syndrome (B-CS) is a spontaneous and fatal disease caused by thrombosis or primary disease of the venous wall that leads to obstruction of the hepatic venous outflow. The demographic and clinical characteristics of patients with Budd-Chiari syndrome are extremely diverse and non-specific. Typical clinical signs such as abdominal pain, fever, liver enlargement, and recent ascites are rare, and the absence of clinical manifestations often occurs. Current conventional treatments can only relieve symptoms but cannot cure them. At present, various abnormal laboratory results are not specific for the diagnosis of B-CS, such as significantly elevated serum transaminases, elevated serum-ascites albumin, and Child-Pugh liver function grades (A, B, and C). The most specific imaging test in clinical practice is CT or MRI after arterial, portal, and late injection of vascular contrast agents, with the presence of obstructive stenosis abnormalities in the hepatic veins and / or the inferior vena cava as the basis for the diagnosis of Budd-Chiari syndrome. However, the examination requires the most useful description of the blocked blood vessels, which places high demands on doctors. At the same time, adverse reactions to contrast agents, the examination level of radiologists, the high cost of imaging examinations, and the low penetration rate of high-resolution imaging equipment are all factors that limit the diagnosis of Budd-Chiari syndrome.

[0003] The proteome is the collection of all proteins in a cell, tissue or organism. The discipline that studies the expression, pattern and functional pattern of all proteins in an organism is called proteomics. It can use efficient protein separation technology and high-resolution protein identification technology to study life substances at the protein level in an overall, dynamic and quantitative manner, and can analyze in detail the complex system of differential protein expression of organisms under different physiological and pathological conditions. Proteomics technology can quickly and accurately screen out differential proteins between healthy people and disease groups from different samples (serum, cerebrospinal fluid, lesion tissue) to gain a deep understanding of biological processes. Therefore, obtaining biomarkers for Budd-Chiari syndrome based on proteomics is crucial for the screening and diagnosis of Budd-Chiari syndrome. Summary of the invention

[0004] In view of this, the present invention proposes a Budd-Chiari syndrome biomarker, a reagent for detecting the Budd-Chiari syndrome biomarker, and an application of the Budd-Chiari syndrome biomarker.

[0005] To achieve the above object, the present invention adopts the following technical solutions: The Budd-Chiari syndrome biomarkers of the present invention include any one or a combination of any two or more of IGF1, SEPRIND1, SEPRINA4, VCAM1 and ICAM1.

[0006] In a preferred embodiment of the present invention, the Budd-Chiari syndrome biomarkers include any one or a combination of SEPRIND1 and SEPRINA4.

[0007] In a more preferred embodiment of the present invention, the Budd-Chiari syndrome biomarker of the present invention includes any one or a combination of two of SEPRIND1 and SEPRINA4, and also includes at least one of IGF1, VCAM1 and ICAM1 (that is, it can be any one, any two, or all three).

[0008] The present invention also proposes a reagent for detecting a biomarker of Budd-Chiari syndrome, wherein the biomarker of Budd-Chiari syndrome includes any one or a combination of any two or more of IGF1, SEPRIND1, SEPRINA4, VCAM1 and ICAM1. Preferably, the biomarker of Budd-Chiari syndrome includes any one or two of SEPRIND1 and SEPRINA4. More preferably, the biomarker of Budd-Chiari syndrome also includes at least one of IGF1, VCAM1 and ICAM1.

[0009] The present invention also provides the use of Budd-Chiari syndrome biomarkers or reagents for detecting Budd-Chiari syndrome biomarkers in the preparation of products for risk prediction, diagnosis, screening or prognosis evaluation of Budd-Chiari syndrome.

[0010] In actual production, the product of the present invention is preferably a kit.

[0011] Compared with the prior art, the advantages of the present invention are: the Budd-Chiari syndrome marker of the present invention, as a blood marker, can be used for the preparation of risk prediction, diagnosis, screening or prognosis evaluation products of Budd-Chiari syndrome, can detect and help improve the diagnostic accuracy of Budd-Chiari syndrome at an early stage, reduce the missed diagnosis rate and misdiagnosis rate of the disease, and is expected to realize the identification and screening of Budd-Chiari syndrome population in large-scale applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 : is the analysis diagram of protein identification of five sample groups in the present invention. Figure 1 In the figure, each circle represents the number of proteins identified by one sample group, the overlapping part represents the number of proteins commonly identified by multiple sample groups, and the non-overlapping part represents the number of proteins specifically identified by the corresponding sample group.

[0013] Figure 2is the PC principal component analysis diagram (three-dimensional) of the present invention. Figure 2 In the figure, the first principal component PC1 is represented by the horizontal axis, the second principal component PC2 is represented by the vertical axis, and the third principal component PC3 is represented by the vertical axis.

[0014] Figure 3 is a statistical diagram of differentially expressed proteins of the present invention. Figure 4 In the figure, blue indicates the number of differentially expressed proteins that were significantly downregulated, and red indicates the number of differentially expressed proteins that were significantly upregulated.

[0015] Figure 4 is a bar chart of KEGG enrichment of differentially expressed proteins of the present invention.

[0016] Figure 5 is the receiver operating characteristic (ROC) curve of the differentially expressed proteins. DETAILED DESCRIPTION

[0017] The following is a detailed description of an embodiment of the present invention in conjunction with the accompanying drawings. This embodiment is implemented based on the technical solution of the present invention, but the protection scope of the present invention is not limited to the following embodiment.

[0018] It should be pointed out that, in the present invention, the term "diagnosis" refers to finding out whether a patient has suffered from a disease or condition in the past, at the time of diagnosis, or in the future, or to finding out the progression of a disease or its possible future progression; "prognosis assessment" refers to assessing a patient's response to treatment and the risk of developing the disease in the future; and "risk prediction" refers to finding out the probability of a patient suffering from a disease or condition at the time of diagnosis or in the future.

[0019] The present invention provides a Budd-Chiari syndrome biomarker, comprising any one of IGF1, SEPRIND1, SEPRINA4, VCAM1 and ICAM1 or a combination of any two or more thereof.

[0020] More preferably, the Budd-Chiari syndrome biomarker of the present invention includes any one or a combination of two of SEPRIND1 and SEPRINA4. More preferably, it also includes at least one of IGF1, VCAM1 and ICAM1 (that is, it can be any one, any two, or three).

[0021] The present invention also proposes a reagent for detecting a biomarker of Budd-Chiari syndrome, wherein the biomarker of Budd-Chiari syndrome includes any one or a combination of any two or more of IGF1, SEPRIND1, SEPRINA4, VCAM1 and ICAM1. Preferably, the biomarker of Budd-Chiari syndrome includes any one or two of SEPRIND1 and SEPRINA4. More preferably, the biomarker of Budd-Chiari syndrome also includes at least one of IGF1, VCAM1 and ICAM1.

[0022] The present invention also provides the use of a Budd-Chiari syndrome biomarker or a reagent for detecting a Budd-Chiari syndrome biomarker in the preparation of a product for risk prediction, diagnosis, screening or prognosis assessment of Budd-Chiari syndrome. In actual production, the product of the present invention is preferably a kit to achieve large-scale detection.

[0023] Embodiment The method for screening biomarkers of Budd-Chiari syndrome of the present invention comprises the following contents: S1. Obtain blood samples from Budd-Chiari syndrome and healthy subjects Blood samples were divided into five groups according to whether patients with Budd-Chiari syndrome had received treatment in the past and the liver function grade (i.e., Child-Pugh) evaluation results: untreated grade A Budd-Chiari syndrome group (untreated_A, n=20), untreated grade B and C Budd-Chiari syndrome group (untreated_BC, n=20), treated grade A Budd-Chiari syndrome group (treated_A, n=20), treated grade B and C Budd-Chiari syndrome group (treated_BC, n=20), and normal healthy group (Control, n=20); blood samples of the four disease groups, treated_A, treated_BC, untreated_A, and untreated_BC, were obtained from the First Affiliated Hospital of Zhengzhou University; S2, performing DIA-MS detection on the sample group in step S1 S21, blood samples were separated into serum, and the separated serum was incubated and separated using a Top column: 10% DOC (final concentration was 0.5%) was first added to the Top column, and then 5 μl of serum sample was added; the Top column was manually shaken horizontally so that all beads in the column could rotate when shaken up and down, and then the column was moved to a rotator, rotated at 10 rpm, and incubated at room temperature for 1 hour; after the incubation was completed, it was transferred to a new centrifuge tube, centrifuged at 1000g for 3 minutes at room temperature, and the filtrate was obtained to remove the high-abundance proteins in the serum; S22, add 2μl DTT (final concentration 10mM) and 10μl 1M ABC to the filtrate in S21 (take 200μl), adjust the pH value to 8.5, mix well, heat at 100℃ for 5min, cool to room temperature, add 1.5μg pancreatin, and digest at 37℃ overnight; after digestion, add 20μl FA (final concentration 1%), mix well, centrifuge at 14000g for 10min at room temperature, repeat once, and take the supernatant; S23, desalting of the supernatant peptides obtained in S22: activation with 80 μl 100% ACN twice, centrifugation at 2000 g for 1 min; equilibration with 80 μl 0.1% FA twice, centrifugation at 3300 g for 1 min; loading, 80 μl each time, centrifugation at 3300 g for 1 min; washing the sample with 80 μl 0.1% FA twice, centrifugation at 3300 g for 1 min; elution with 80 μl 50% ACN + 0.1% FA once, drying at 1800 rpm and 45 °C (60 min), and storing at -80 °C for later use; S24, fractionation of mixed samples for library construction: In order to establish a serum proteome spectral library with deep coverage, the peptides after removal of high-abundance protease cleavage were fractionated using a strong cation exchange column (SCX) and a high-pH reversed-phase C18 column. Each sample for library construction obtained 12 fractions, and a total of 60 components were used to establish the spectral library, thereby obtaining a serum proteome spectral library with deep coverage; Strong cation exchange (SCX) fractionation: resuspend the peptides (obtained from S23) with 80 μl 1% formic acid solution, add the resuspended solution to the micro-pipette tip SCX column, collect the flowthrough, and then elute the labeled peptides with 80 μl 20 mM, 50 mM, 100 mM, and 1000 mM NaCl solutions, respectively. Mix the eluates into two fractions, namely SF1: flowthrough + 20 mM eluent + 100 mM eluent, SF2: 50 mM eluent + 1000 mM eluent; High pH C18 reverse phase column fractionation: adjust the pH of SF1 and SF2 to 10 with 12.5% ​​ammonia water, then add them to two different C18 reverse phase columns, and use 80μl 6%, 9%, 12%, 15%, 18%, 21%, 25%, 30%, 35% ACN solution (pH=10) to elute the peptides in turn, and mix the fractions corresponding to SF1 and SF2 into six fractions: CF1: 6%+25%, CF2: 9%+30%, CF3: 12%+35%, CF4: 15%, CF5: 18%, CF6: 21%. Drain the fractions and store them at -80℃ for later use; S25, DIA-MS analysis of the peptide sample of S24, where the HPLC conditions were as follows: the HPLC system was Easy-nLC 1200 (Thermo Fisher Scientific, USA), the mobile phase A was 0.1% formic acid in water, and the mobile phase B was 80% acetonitrile + 0.1% formic acid solution; Before analysis, the peptide samples were dissolved in 0.1% formic acid aqueous solution, and iRT peptides were added for retention time correction. The samples were mixed evenly by oscillation, centrifuged at 10000 g for 10 min, 5 μl of the sample was loaded, and separated using a C18 reverse phase column at a flow rate of 600 nl / min and gradient elution for 120 min. The mass spectrometry conditions were as follows: data were collected using an Orbitrap Fusion Lumos (Thermo Fisher Scientific, USA) mass spectrometer, with a nanoelectrospray ion source (NSI), a spray voltage of 2200 V, and an ion transfer capillary temperature of 320 °C; the DIA (data-independent acquisition mode) method was used for acquisition: the first stage was full scan using Orbitrap, Scan Range (m / z): 400–1200, Orbitrap Resolution: 120,000, AGCtargets: 4e5, Maxinjection time: 50 ms; the second stage was an isolation window of 25 m / z (1 m / z overlap between windows, the actual isolation window was set to 26 m / z, and a total of 32 windows), the parent ion was fragmented by 32% of the high-energy collision dissociation energy (HCD), and the fragment ions were detected in the Orbitrap, Orbitrap Resolution: 30,000, AGCtargets: 1e5, Maxinjection time: 54ms, one level scan is inserted between each DIA cycle; S26, DIA-MS data acquisition: Sequest embedded in Proteome Discoverer 2.2 (Thermo Scientific) was used to search the database, and the database was Uniprot human protein library); the parameters were set as follows: the parent ion mass deviation was 20 ppm, and the secondary fragment ion mass deviation was 0.05 Da; The trypsin cleavage site allows two missed cleavage sites; the dynamic modification is Protein Acetyl (protein N-term), Oxidation (M), Carbamidomethyl (C), and the peptide protein level FDR (false discovery rate) is controlled to be less than 1% through the decoy database; DIA data were analyzed using Skyline, and the isolation window was set according to the DIA acquisition parameters. All peptides and corresponding proteins in the spectral library were used as targets. The five b / y ions with the highest intensity were selected for peak extraction for each peptide. The peak extraction results were evaluated for false positives using mProphet, and the Q value was controlled to be <0.01.

[0024] The number of proteins identified in the above five sample groups was counted and displayed in a Venn diagram. The results are shown in Figure 1 .Depend on Figure 1 It can be seen that a total of 640 proteins were identified in the five sample groups, and the number of common proteins identified in the five sample groups was 578; 35 proteins were identified in four sample groups, such as the number of common proteins identified in the treated_A group, the treated_BC group, the untreated_A group and the untreated_BC group was 15; 15 proteins were identified in three sample groups, such as the number of common proteins identified in the untreated_A group, the treated_A group and the treated_BC group was 4; 11 proteins were identified in two sample groups, such as the untreated_A group and the treated_A group had one protein in common; the untreated_A group had one specific protein; S3, principal component analysis (i.e. PCA analysis) of the identified proteins Principal component analysis (PCA) is an unsupervised data statistical analysis method. It forms a new set of comprehensive statistical variables by rearranging all identified metabolites linearly, and selects several comprehensive variables from them to make them reflect the vertical and horizontal information of the original variables as fully as possible, thereby achieving the purpose of reducing dimensionality and accurate analysis.

[0025] This example reduces the dimensionality of a large amount of protein expression information contained in the blood samples into a few independent principal components, and retains the original data information as much as possible to compare samples, find outlier samples, and identify sample clusters with high similarity. The PCA analysis results of the disease group (including untreated_A, untreated_BC, treated_A and treated_BC) and the normal healthy group are shown in Figure 2 .Depend on Figure 2 It can be seen that the four sample groups of Budd-Chiari syndrome can be well distinguished from the normal healthy group.

[0026] S4, differentially expressed protein analysis: The ratio of all biological measurement means of each protein in two compared sample groups was taken as the fold change (FC). In order to determine the statistical significance of the difference, the quantitative value of each protein in two compared sample groups was subjected to T-test, and the corresponding P-value was calculated as a significance indicator, and the P-value was corrected using BH FDR, and FC>1.5 times and P-value<0.05 were used as the conditions for screening differentially expressed proteins.

[0027] The statistics of the number of significantly up-regulated and down-regulated differentially expressed proteins in the five sample groups are shown in Figure 4 .Depend on Figure 4 It can be seen that compared with the Control group, 86 proteins in the untreated_A group were significantly upregulated and 36 proteins were significantly downregulated; compared with the Control group, 171 proteins in the untreated_BC group were significantly upregulated and 89 proteins were significantly downregulated; compared with the Control group, 109 proteins in the treated_A group were significantly upregulated and 47 proteins were significantly downregulated; compared with the Control group, 159 proteins in the treated_BC group were significantly upregulated and 70 proteins were significantly downregulated; compared with the Control group, a total of 162 proteins were significantly upregulated and 49 proteins were significantly downregulated in the four disease groups; S5, functional enrichment analysis of differentially expressed proteins (using KEGG analysis): KEGG analysis is an information network that connects known molecular interactions, such as metabolic pathways, complexes, and biochemical reactions. KEGG Pathway significant enrichment analysis uses KEGG Pathway as a unit and applies hypergeometric test to find pathways that are significantly enriched in differentially expressed proteins compared to all identified protein backgrounds. Functional enrichment analysis was performed on differentially expressed proteins, and the enrichment analysis was sorted from small to large according to the P value. The top 20 significant pathways (P < 0.02) were taken to draw a bar graph of enriched KEGG entries. The results are shown in Figure 4 ; S6, perform AUC analysis on the proteins in step S5: The ROC curve is a comprehensive index of sensitivity and specificity continuous variables, and the correlation between sensitivity and specificity is reflected by the composition. Draw the ROC curve for the proteins screened in S5, and calculate the area under the curve (AUC) to determine the diagnostic value of differential expression. The specific judgment method is: if the AUC is below 0.75, it means that the diagnostic accuracy is low; if the AUC is above 0.75, it can meet the requirements of clinical diagnosis.

[0028] The AUC analysis results showed that there were 12 proteins with AUC values ​​above 0.75 (see Figure 5 The 12 proteins were as follows: VCAM1 (Vascμlar Cell Adhesion Molecule 1, P19320), COL6A3 (Collagenalpha-3, P12111), B2M (Beta-2-microglobμlin, P61769), SERPIND1 (Heparin cofactor 2, P05546), CSF1R (Cathepsin D, P07339), VWF (von Willebrandfactor, P04275), CSF1R (Macrophagecolony-stimμlating factor 1 receptor, P07333), SERPINA4 (Kallistatin, P29622), CST3 (Cystatin-C, P01034), F5 (Coagμlation factor V, P12259), IGF1 (Insμlin-like growth factor I, P05019) and ICAM1 (Intercellμlar adhesion molecule 1). molecule 1, P05362), their AUC values ​​ranged from 0.9625 to 0.77625, and the protein with the highest AUC was VCAM1 (AUC = 0.9625, encoded by the vascular cell adhesion molecule 1 gene); S7, PRM (Parallel Reaction Monitoring) was used to verify the differentially expressed proteins screened in step S5: serum samples of patients with Budd-Chiari syndrome (20 samples in total) and normal controls (5 samples in total) were collected again, including untreated patients with grade A Budd-Chiari syndrome (untreated_A, n=5), untreated patients with grade B and grade C Budd-Chiari syndrome (untreated_BC, n=5), treated patients with grade A Budd-Chiari syndrome (treated_A, n=5), treated patients with grade B and grade C Budd-Chiari syndrome (treated_BC, n=5), and normal controls (Control, n=5); the PRM platform was biologically validated for the 12 proteins evaluated in S6, and the peptide fragments with the highest detection frequency and no modification among the 12 target proteins were selected as the targeted detection peptides of the 12 target proteins. The results are shown in Table 1.

[0029] Table 1 PRM verification results of 12 target proteins As shown in Table 1, IGF1 (AUC = 0.804), SEPRIND1 (AUC = 0.9275), SEPRINA4 (AUC = 0.79), VCAM1 (AUC = 0.9625) and ICAM1 (AUC = 0.842) have been well validated and can be used as biomarkers for the diagnosis, screening and prognosis evaluation of Budd-Chiari syndrome.

Claims

1. A biomarker for Budd-Chiari syndrome, characterized in that: It includes any one or a combination of any two or more of IGF1, SEPRIND1, SEPRINA4, VCAM1 and ICAM1.

2. The Budd-Chiari syndrome biomarker according to claim 1, characterized in that: Includes any one or a combination of SEPRIND1 and SEPRINA4.

3. The Budd-Chiari syndrome biomarker according to claim 1, characterized in that: It also includes at least one of IGF1, VCAM1 and ICAM1.

4. A reagent for detecting a biomarker of Budd-Chiari syndrome, characterized in that: The biological markers of Budd-Chiari syndrome include any one or a combination of any two or more of IGF1, SEPRIND1, SEPRINA4, VCAM1 and ICAM1.

5. The reagent for detecting a biomarker of Budd-Chiari syndrome according to claim 3, characterized in that: The Budd-Chiari syndrome biomarkers include any one or both of SEPRIND1 and SEPRINA4.

6. The reagent for detecting a biomarker of Budd-Chiari syndrome according to claim 4, characterized in that: The Budd-Chiari syndrome biomarker also includes at least one of IGF1, VCAM1 and ICAM1.

7. Use of the biomarker according to any one of claims 1 to 3 or the reagent according to any one of claims 4 to 6 in the preparation of a product for risk prediction, screening or prognosis assessment of Budd-Chiari syndrome.

8. The use according to claim 7, characterized in that: The product is a test kit.

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