CN110824167B - Application of marker for predicting prognosis of acute liver failure in preparation of acute liver failure prognosis prediction assessment kit - Google Patents
Application of marker for predicting prognosis of acute liver failure in preparation of acute liver failure prognosis prediction assessment kit Download PDFInfo
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Abstract
The invention discloses an application of a marker for predicting prognosis of acute liver failure in preparing a kit for predicting prognosis of acute liver failure, wherein the marker for predicting prognosis of acute liver failure comprises complement factor B, a plasma proteinase C1 inhibitor, a blood coagulation factor XII and apolipoprotein C-1. The first proposed comparative proteomic analysis of HBV-ACLF provides high throughput iTRAQ analysis of surviving and dying patients for HBV-ACLF patients on the day of hospitalization and 4 weeks after treatment, provides new insight for proteomic profile of HBV-related ACLF patients with completely different prognosis, and determines that four proteins, complement Factor B (CFB), plasma proteinase C1 inhibitor (PPC 1), coagulation factor XII (CF 12) and apolipoprotein C-1 (APO-CI), can be used as prognostic biomarkers for HBV-ACLF.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to application of ITIH3 and APO-C1 in preparing an ELISA kit for predicting prognosis evaluation of acute liver failure.
Background
Acute Chronic Liver Failure (ACLF) is a condition belonging to the subset of cirrhosis patients who develop organ failure after hospitalization for hepatitis b virus infection. HBV-associated ACLF is the primary risk factor for liver failure [1-3] . Inflammatory diseases are considered to be a major factor in the development of ACLF, although the pathophysiology of this condition is not yet understood [4,5] . ACLF is characterized by rapid progression, leading to short-and medium-term mortality rates as high as 50-90% [6] . The outcome of this syndrome is estimated by systemic cardiac and hepatic hemodynamics [7,8] . However, if an ACLF patient receives appropriate treatment, it may restore the original liver function state [9-11] . There is an urgent need for a prognosis for patients with predicted mortality ACLF or a developing factor for optimal treatment.
Unfortunately, no effective markers have been reported to predict prognosis of ACLF patients after standard treatment procedures. Most previous studies have focused on targeting molecules as potential factors for predicting ACLF prognosis, especially inflammatory factors [12-14] . However, only a few others in other independent studies are supported.
HBV-induced ACLF is the main type of Chinese liver failure and is also the main cause of death in patients with liver disease [23] . The China society of medicine (CMA) developed guidelines for diagnosis and treatment of liver failure in 2006 and updated the revision in 2012 [24] . However, ACLF differs from us and europe in definition and treatment strategies in chinese liver failure diagnosis and treatment guidelines. The performance of available scoring systems has also proven unsuitable for Chinese ACLF patients, including MELD, SOFA and APACHE [25-27] . Unfortunately, a significant fraction of HBV-associated ACLF patients respond poorly to standard therapy and die shortly after hospitalization. Therefore, finding new biomarkers to predict prognosis of ACLF patients is of great value.
Over the past few decades, tremendous efforts have been made to develop early diagnostic and prognostic biomarkers for HBV-ACLF. However, most of them are inflammatory and/or immune-related factors (IL-6, IL-8,SIRS,solube ST2, cystatin C, neutrophil gelatinase-associated lipocalin), which may be associated with various disorders, rather than HBV-ACLF, leading to poor utility [28-32] . Proteomics is a powerful technique for finding potential biomarkers and is widely used for tumors, cardiovascular diseases and other organ dysfunction [15-20] . However, only two groups performed proteomic studies on prognostic markers of HBV-associated ACLF, one using dielectrophoresis (2-DE) and the other using isobaric tagsRelative and Absolute Quantification (iTRAQ) technique [21，22] 。
In 2010 Ren et al identified 23 differentially expressed proteins by 2-DE in normal, chronic hepatitis B and ACLF samples and further validated that alpha-1-acid glycoprotein might be a candidate marker for prognosis of HBV infection [21] . In 2017, zhou et al performed high throughput iTRAQ analysis of plasma from healthy controls, chronic hepatitis B and HBV-ACLF patients, identified 42 differentially expressed proteins, and further confirmed the difference in expression levels of 6 proteins as potential HBV-ACLF biomarker candidates [22] . However, neither person correlated the proteomic data with post-treatment results.
Reference is made to:
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disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an application of a marker for predicting the prognosis of acute liver failure in preparing a prognostic evaluation kit for predicting acute liver failure.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
use of a marker for predicting the prognosis of acute liver failure comprising complement factor B, a plasma protease C1 inhibitor, coagulation factor XII and apolipoprotein C-1 in the preparation of a prognostic evaluation kit for predicting the prognosis of acute liver failure.
The invention has the beneficial effects that: the first proposed comparative proteomic analysis of HBV-ACLF provides high throughput iTRAQ analysis of surviving and dying patients for HBV-ACLF patients on the day of hospitalization and 4 weeks after treatment, provides new insight for proteomic profile of HBV-related ACLF patients with completely different prognosis, and determines that four proteins, complement Factor B (CFB), plasma proteinase C1 inhibitor (PPC 1), coagulation factor XII (CF 12) and apolipoprotein C-1 (APO-CI), can be used as prognostic biomarkers for HBV-ACLF.
Drawings
FIG. 1 is a schematic diagram of the experimental procedure for studying proteome spectra of HBV-ACLF patients in the living and dead groups in example 2;
FIG. 2 is a volcanic plot of DEP identified in example 2 from S-H and D-H, S-F and D-F, S-H and S-F, and D-H and D-F;
FIG. 3 is a Venn diagram and heat map analysis of all DEPs identified from S-H and D-H, S-F and D-F, S-H and S-F, and D-H and D-F in this example 2;
FIG. 4 is a schematic representation of the results of a KEGG pathway enrichment analysis of DEP identified from S-H and D-H, S-F and D-F, S-H and S-F, and D-H and D-F in example 2;
FIG. 5 is a schematic diagram showing the pathway enrichment analysis of S-H and D-H DEP in the cell component class of example 2;
FIG. 6 is a schematic diagram showing protein-protein interaction (PPI) network analysis of DAP for S-H and D-H (A), S-F and D-F (B) in example 2;
FIG. 7 is a schematic diagram showing the result of ROC analysis of the differential gene amplified sample obtained in example 2.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, and it should be noted that, while the present embodiment provides a detailed implementation and a specific operation process on the premise of the present technical solution, the protection scope of the present invention is not limited to the present embodiment.
Example 1
The present embodiment provides the use of a marker for predicting the prognosis of acute liver failure, including complement factor B, a plasma protease C1 inhibitor, coagulation factor XII and apolipoprotein C-1, in the preparation of a prognostic evaluation kit for predicting the prognosis of acute liver failure.
By the kit, the expression levels of complement factor B, a plasma proteinase C1 inhibitor, a blood coagulation factor XII and apolipoprotein C-1 in serum of HBV-ACLF patients can be detected, so that prognosis of acute liver failure can be predicted.
Example 2
This example verifies the technical scheme of example 1 by experiment.
1. Experimental design and clinical characteristics of HBV-ACLF patients
Based on inclusion exclusion criteria, 24 patients were enrolled in the study, including 11 survivors (group S) and 13 deaths (group D). The clinical characteristics of these patients are shown in table 1.
TABLE 1 clinical information of HBV-associated ACLF patients
* P value <0.05; * P value <0.01. White blood cells, whole blood cells; plt, platelets; ALT, glutamate-pyruvate transaminase; TBil, total billrubin; CHE, cholinesterase; albumin; cr, creatinine; PT, prothrombin time; PTA, prothrombin activity; INR, international normalized clotting time ratio; FIB, fibrinogen; child-tune-Pugh; MELD, model of end-stage liver disease.
As can be seen from table 1, all the factors examined were not significant between the surviving and the dying groups except for prothrombin time (PTs, p=0.021), international normalized ratio (INR, p=0.008) and end-stage liver disease model (MELD, p=0.001).
In this example, the experimental workflow is shown in FIG. 1. After depletion of high abundance proteins, the S-group serum protein samples (S-H) obtained on the day of hospitalization were split into two pooled duplicate samples and labeled 113 and 114. 115 and 116 tags were used to label two pooled replicates into which group D serum protein samples (D-H) obtained on the day of hospitalization were split. And S group samples (S-F) at 4 weeks after treatment were 117 and 118, and D group samples (D-F) were 119 and 121. The isobaric tag-labeled peptides were then put together for high throughput LC-MS/MS analysis.
2. Differentially expressed proteins identified by iTRAQ
A total of 519 proteins were identified by iTRAQ, of which 280 were considered high quality quantitative proteins (95% with confidential peptides with quantitative information.gtoreq.2). The significant differential expression threshold was set to a change of at least 2-fold and p <0.05. Volcanic images show the distribution pattern of all high quality proteins between S-H and D-H samples (FIG. 2A), between S-F and D-F (FIG. 2B), between S-H and S-F (FIG. 2C), between D-H and D-F (FIG. 2D). Significant proteins are represented as light spots, while non-significant proteins are represented as dark spots.
111 Differentially Expressed Proteins (DEPs) were identified, with 42 DEPs identified between S-H and D-H, 55 identified between S-F and D-F, 51 identified between S-H and S-F, and 36 identified between D-H and D-F, as shown in FIG. 3A. A thermal map of all 111 DEPs is also provided, as shown in fig. 3B. Minimal DEP was identified between the D-H and D-F samples (36), indicating poor treatment outcome for these patients.
3. Bioinformatics analysis of DEPs
Notably, six proteins were identified simultaneously in DEP of S-H and D-H and S-F and D-F, but not in DEP of S-H and S-F and D-S and D-F (as shown in FIG. 3A), which were complement factor H, alpha-trypsin inhibitor heavy chain H3, complement factor B, plasma protease C1 inhibitor, coagulation factor XII and apolipoprotein C-1, respectively. These six proteins should be expressed at consecutive levels differentially between patients in group S and group D, not only on the day of hospitalization, but also 4 weeks after treatment, indicating that they are stably differentially expressed between surviving and dying HBV-ACLF patients. Thus, six proteins can be considered as candidate markers for predicting prognosis of HBV-ACLF patients. Among the six potential candidate markers, complement factor H and alpha-trypsin inhibitor heavy chain H3 exhibited reduced expression levels after four weeks of treatment in group S, indicating their levels of instability before and after treatment. However, complement factor B, plasma protease C1 inhibitor, coagulation factor XII and apolipoprotein C-1 showed non-significant expression levels before and after treatment in both group S and group D, indicating that they are promising candidate markers. The protein identification information of the six potential candidate markers is shown in Table 2, table 2
1 Average normalized expression level for each group.
To better understand the function of these DEPs, the present example performed KEGG ontologies, gene Ontologies (GO) and protein-protein interaction network (PPI) analyses. KEGG pathway enrichment analysis showed that the most abundant pathways (percentage of test group compared to reference set) were complement and coagulation cascade of S-H versus D-H, S-F versus D-FS-H versus S-F as shown in figures 4A-4C, respectively, while african trypanosomiasis was DS versus D-F as shown in figure 4D. Again, the results indicate that the death group has a different response to the standard treatment routine.
DEP between S-H and D-H has the most important biological value in view of experimental design, for predicting prognosis of HBV-ACLF patients. For the cellular component class, GO enrichment analysis of DEP between S-H and D-H is shown in FIG. 5. The branch ends of the tree view of the GO term are hemoglobin complex (GO: 0005833), endocytic vesicle lumen (GO: 0071682), synaptic vesicle (GO: 0008021), dendrite (GO: 0030425), basolateral plasma (GO: 0016323) and membrane raft (GO: 0045121), indicating the most significant differences between proteomic profiles of surviving and dying HBV-ACLF patients. Diagnostics are located in vesicle and membrane related cellular components.
In fig. 5, the significantly enriched (FDR p value < 0.05) GO term is shown in the color box. Maturation of yellow is associated with the level of significance of each term. The meaning of the arrow types is as follows: red arrows represent two important terms; black arrows represent one important term and one non-important term; black dashed arrows link two non-important terms.
The protein-protein interaction network of a particular DEP set may reflect the pattern of biological factors of interest affecting the serum sample. For the purposes of this example, PPI analysis of S-H and D-H, S-F and D-F is shown, as shown in FIG. 6A and FIG. 6B, respectively, with the lowest interaction score required set to the highest confidence level (0.900) in the STRING database in FIG. 6, and with the disconnected nodes in the network hidden. Proteins associated with vesicle and membrane cell components have key nodes of DEP between groups S and D before and after treatment. Notably, complement and coagulation-related proteins also show important roles in the network.
4. ROC analysis of the screened differential Gene amplified samples
The sample size was expanded (death n=10, survival n=9), and the expression levels of 8 factors Protein S, ITIH4, ITIH3, F12, CFH, CFB, APO-E, APO-C1 in serum were verified by ELISA, as shown in fig. 7.
In FIG. 7, FIG. 7A shows the distribution of APO-C1 in the survival and death groups, FIG. 7B shows the distribution of Protein S in the survival and death groups, and FIG. 7C shows the distribution of ITIH3 in the survival and death groups. * P <0.05. Surviving and dead groups were tested using Student's t. The data are mean+ -SD expression of APO-C1, protein S, ITIH 3. Fig. 7D shows the ITIH3 ROC curve with an area under the curve (AUC) of 82.22%. FIG. 7E shows an APO-C1 ROC curve with an area under the curve (AUC) of 77.78%. FIG. 7F shows the ROC curve for Protein S with an area under the curve (AUC) of 75.56%. FIG. 7G shows the ROC curve of the ITIH3+APO-C1 two-factor combination assay with an area under the curve (AUC) of 92.22%
As can be seen from FIG. 7, there is a significant correlation between the ROC curve and prognosis with three factors, protein S, ITIH3, APO-C1, respectively, with an area under the curve greater than 0.75. By further carrying out the association analysis of the double factors, the ROC curve of the association analysis of the ITIH3 and APO-C1 double factors has extremely obvious correlation with the prognosis of acute and chronic liver failure, and the area under the curve is 0.922. The data show that ITIH3 and APO-C1 combined analysis can well predict the prognosis of acute liver failure.
5. Summary
In this example, the first proposed HBV-ACLF comparative proteomic analysis provides a high throughput iTRAQ analysis of surviving and dying patients for HBV-ACLF patients on the day of hospitalization and 4 weeks after treatment. This experimental design will help identify new proteins that are associated with the prognostic performance of HBV-ACLF patients following standard treatment.
In this example 518 proteins were identified from the plasma of surviving and dying HBV-ACLF patients, which was more than any other study. Stringent threshold conditions (95% confidential peptide, quantitative information. Gtoreq.2) are used for protein identification to ensure reliability of subsequent analysis, yielding 280 high quality proteins. 42,55,51 and 36 DEPs were identified between S-H and D-H, S-F and D-F, and S-H and S-F, D-H and D-F, respectively. Minimal DEP was identified between the D-H and D-F samples (36), indicating poor treatment outcome for these patients. However, six proteins expressed differently in S-H and D-H, S-F and D-F show potential useful for predicting HBV-ACLF prognosis.
In groups S and D, four proteins, complement Factor B (CFB), plasma proteinase C1 inhibitor (PPC 1), coagulation factor XII (CF 12) and apolipoprotein C-1 (APO-CI), show stable expression levels both before and after treatment and are differentially expressed between groups S and D, making them ideal biomarker candidates for predicting HBV-prognosis.
Complement factor 3 is reported to be an independent risk factor for mortality in HBV-ACLF patients [14] . Coagulation disorders and hemostasis play an important role in liver disease [33] . Apolipoproteins have been reported to be involved in various forms of liver disease and liver transplantation, in particular apolipoproteins A-V and A-IV [34-36] . Furthermore, another iTRAQ-based proteomic analysis showed that complement factors and apolipoproteins are involved in liver failure in hepatitis b progression [37] 。
Bioinformatic analysis of DEP for S-H and D-H showed that complement and coagulation cascade are the most significant enrichment pathways, consistent with previous studies. In addition, DEPs of D-H and D-F showed different GO and KEGG functional distribution patterns than other DEPs, as well as different PPI network structures, indicating that the plasma proteome profile of this group of patients was changed differently compared to the other groups after the standard. This phenomenon suggests the importance of developing novel biomarker candidates for predicting HBV-ACLF prognosis.
6. Materials and methods
6.1 clinical samples
From 1 month 2014 to 2 months 2017, 45 HBV-ACLF patients are admitted to the department of hepatology of the affiliated traditional Chinese medical hospital of Xinjiang medical university. After the exclusion, 24 patients were selected for high-throughput proteomic study, 11 of which were surviving and 13 of which were dead groups, based on the treatment results. All patients received standard treatment according to diagnostic and therapeutic guidelines for liver failure (2012 edition). Plasma samples were obtained for each patient on the day of hospitalization (H) and four weeks after treatment (F).
The study in this example was approved by the medical ethical committee of Uygur autonomous region in Xinjiang (ethical committee file number: 2013XE 0137). Prior to registration, all participants obtained written informed consent.
6.2 protein extraction and iTRAQ analysis
Protein extraction, trypsin digestion and iTRAQ labelling were performed as described previously [38] . In short, use
Immunoaffinity albumin and IgG depletion kits (Sigma-Aldrich, S-Hanghai, china) deplete high abundance proteins in plasma, including albumin and IgG. The collected solution was cleaved in a cleavage buffer (7M urea, 2M thiourea, 50mM Tris,50mM DTT and 1mM PMSF) and the protein content was determined using the BraD-Ford assay (Bio-Rad, beijin, china). Following overnight FASP trypsin digestion with modified trypsin (catalog No. V5113 Promega, shanghai, china), iTRAQ labeling was performed according to the manufactured construct (Applied Biosystems, S-Hanghai, china). Isotopic labels 113 and 114 are used to label S-H samples, 115 and 116 are used to label D-H samples, 117 and 118 are used to label S-F samples, 119 and 121 are used to label D-F samples.
The FASP digested peptides were separated into 15 fractions by High Performance Liquid Chromatography (HPLC) (Ultimate 3000, dionex, thermo, S-Hanghai, china). The eluted peptide fractions were collected and desalted by an off-line fraction collector and C18 Cartridge (Sigma, S-Hanghai, china) prior to MS/MS analysis. MS analysis was performed using a AB SCIEX TripleTOF 5600plus mass spectrometer system (AB SCIEX, S-Hanghai, china).
Proteins were identified by ProteinPilotTM 5.0. The obtained data was automatically searched based on the homo sapiens protein database downloaded from UniProt (2017, 10, 1). The confidence threshold (unused ProtScore) for the protein was set to be over 1.3. At the same time, more than 2 matched peptides had 95% confidence intervals for protein identification.
6.3 bioinformatics analysis
All identified proteins were localized to the gene ontology using BLAST2 GO. GO term enrichment analysis by GOEAST [39] Is carried out. Analysis of protein-protein interaction networks Using STRING [40] 。
6.4 statistical analysis
The iTRAQ data was analyzed using the proteonpilottm 5.0 (AB Sciex, S-Hanghai, china) with a threshold of 95% confidential peptide ≡2 (unused fraction > 1.3). All statistical analyses were performed by SPSS 20.0. When p-value <0.05 and fold change >2, different abundant proteins were determined.
6.5 Elisa experiments and ROC analysis
The expansion of the sample size (dead n=10, surviving n=9) using the Elisa assay verifies the expression of 8 factors Protein S, ITIH4, ITIH3, F12, CFH, CFB, APO-E, APO-C1 in serum and was statistically analyzed using spss 20.0.
7. Conclusion(s)
Taken together, this example presents an iTRAQ-based proteomic analysis of plasma from HBV-ACLF surviving and dying patients on the day of hospitalization and 4 weeks after treatment. A total of 280 high quality proteins were determined and submitted to subsequent analysis. Bioinformatic analysis of KEGG, GO and PPI showed complement and coagulation cascades to be the most significantly enriched pathways. This example identifies four proteins (CFB, PPC1, CF12 and APO-CI) that exhibit stable, varying expression levels between pre-and post-treatment survival and death groups, indicating that they may be biomarker candidates for predicting HBV-ACLF prognosis. The data in this example provides new insight into the proteomic profile of HBV-related ACLF patients with completely different prognosis and determines four proteins as prognostic biomarkers for HBV-ACLF.
Various modifications and variations of the present invention will be apparent to those skilled in the art in light of the foregoing teachings and are intended to be included within the scope of the following claims.
Claims (1)
1. Use of a marker for predicting the prognosis of acute liver failure in the preparation of a serum sample predictive acute liver failure prognosis evaluation kit, characterized in that the marker for predicting the prognosis of acute liver failure comprises complement factor B, a plasma proteinase C1 inhibitor, coagulation factor XII and apolipoprotein C-1.
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