Application of DR5 in preparation of detection system for hepatic failure risk prognosis

Abstract

The invention relates to application of DR5 in preparation of a detection system for hepatic failure risk prognosis. The invention also relates to application of the combination of the DR5, TBIL and INR markers in evaluating the risk of hepatic failure. The DR5 single index and the DR5, TBIL and INR three-index model provided by the invention are especially suitable for liver failure risk prediction on the basis of liver cancer. Compared with an existing model, the DR5 single index model and the DR5, TBIL and INR three-index model are excellent in sensitivity, specificity and area difference under an AUC curve, and a more accurate prediction result can be obtained. At present, part of patients possibly and rapidly develop into hepatic failure even under the condition that liver cirrhosis does not exist, and the hepatic failure phenomenon in the early stage of liver cirrhosis is difficult to detect in time. The DR5 single index and the DR5, TBIL and INR three-index model provided by the invention can solve the problem of difficulty in early accurate diagnosis of ACLF caused by pure dependence on traditional biochemical and blood coagulation indexes.

Description

Technical Field

[0001] The present invention relates to the technical field of disease prediction, and in particular to the application of DR5 in preparing a detection system for liver failure risk prognosis. Background Art

[0002] Hepatocellular carcinoma (HCC) is a common malignant tumor and one of the leading causes of cancer death worldwide. According to data released by the National Cancer Center in 2023, the number of newly diagnosed cases of liver cancer in 2022 reached 368,000, accounting for 42.4% of new cases of liver cancer worldwide; the number of deaths from liver cancer in the same year reached 317,000, accounting for 41.7% of the total number of liver cancer deaths worldwide. Many liver cancer patients cannot achieve radical cure through surgery due to the unresectable nature of the tumor. Unresectable hepatocellular carcinoma is medically divided into two main categories: surgically unresectable and oncologically unresectable. Both situations involve different medical considerations and treatment strategies. "Surgically unresectable" refers to situations where the tumor cannot be safely removed due to anatomical or physiological limitations, including insufficient residual liver volume (when the remaining liver volume after tumor removal is expected to be insufficient to maintain the necessary liver function), tumor location (the tumor may be located in a critical area of ​​the liver, such as close to important vascular structures or bile ducts, making the operation extremely risky), and multiple important blood vessels involved (the tumor may have invaded multiple important liver inflow tracts, such as the hepatic artery, portal vein, or outflow tracts, such as the hepatic vein and inferior vena cava. Damage to these vessels may cause irreversible effects on liver function). In these cases, surgical resection may lead to serious complications and may even make the operation impossible to complete. Therefore, doctors may seek other treatments, such as local ablation, interventional therapy, radiotherapy, or systemic therapy. "Oncologically unresectable" means that even if the tumor can be technically removed, surgical resection may not bring survival advantages to the patient due to the biological characteristics or distribution of the tumor, including multiple tumors (stage IIb HCC with multiple lesions, surgery may not completely remove all tumors, and the surgical risks and the risk of complications during the patient's recovery period are high), vascular cancer thrombus (stage IIIa HCC with cancer thrombus in the main trunk of the inferior vena cava or portal vein, these cancer thrombus may indicate tumor invasiveness and high risk of recurrence), extrahepatic metastasis (some stage IIIb HCC may have extrahepatic metastasis, such as lung metastasis or bone metastasis), etc. In the case of oncologically unresectable, doctors may recommend non-surgical treatments, such as systemic chemotherapy, targeted therapy, immunotherapy, or their combination therapy, in order to achieve the purpose of controlling disease progression, relieving symptoms, and improving quality of life.

[0003] Current medical advances have enriched the methods of non-surgical treatment of hepatocellular carcinoma and improved the survival rate of HCC. Some regimens can successfully convert originally unresectable tumors into surgically resectable states through "downstaging" treatment. However, the prognosis of patients with unresectable HCC varies greatly. This variability is mainly affected by several key factors. First, the reserve capacity of liver function is an important factor in determining the patient's prognosis, which affects the liver's response to the disease and treatment; second, the inflammatory environment of the liver also affects the patient's prognosis, and chronic inflammation may lead to further aggravation of liver damage. In addition, the apoptotic tendency of hepatocytes, that is, the ability of liver cells to self-destruct, is also a factor affecting the patient's prognosis. The interaction of these factors may ultimately lead to the serious clinical condition of acute-on-chronic liver failure (ACLF).

[0004] ACLF is a clinical syndrome triggered by multiple factors on the basis of chronic liver disease. This condition is characterized by the exacerbation of acute jaundice and coagulopathy, and may be accompanied by complications including hepatic encephalopathy, ascites, electrolyte imbalance, infection, hepatorenal syndrome, hepatopulmonary syndrome, and extrahepatic organ failure. The patient's clinical symptoms appear acutely, the jaundice deepens rapidly, and the serum total bilirubin level increases significantly, at least 10 times the upper limit of the normal value, or the daily increase is not less than 17.1μmol / L. In addition, the patient also shows a bleeding tendency, which can be assessed by prothrombin activity (PTA) less than 40% or international normalized ratio (INR) greater than or equal to 1.5. In addition to liver problems, patients have functional failure in at least one extrahepatic organ system (such as the nervous system, circulatory system, respiratory system, or kidney). These symptoms indicate that the patient's condition is serious and has a high risk of short-term death. At present, there is no definite and effective treatment for advanced and rapidly progressive ACLF, but if the potential risk of ACLF can be identified as early as possible, active liver protection treatment can help some patients improve their prognosis. At the same time, studies have shown that for patients with unresectable liver cancer who are at potential risk of liver failure, if active anti-tumor treatment measures such as targeted therapy, immunotherapy or interventional minimally invasive surgery are taken, liver damage may be further aggravated, which may have an adverse effect on the patient's prognosis. Although liver transplantation may be effective for patients with severe common ACLF, the pathological characteristics of HCC, especially ACLF occurring on the basis of HCC, limit the conditions for patients to receive liver transplantation, which increases the complexity of treatment. Therefore, it is extremely important to evaluate the risk of ACLF in liver cancer patients.

[0005] The treatment effect of HCC is largely affected by the liver function reserve. When the liver parenchyma is damaged or the liver function reserve decreases, a series of serological indicators will reflect this change: the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), lactate dehydrogenase (LDH) and total bilirubin (TBIL) increase, while the level of albumin (ALB) decreases, and the coagulation time is prolonged. These key indicators not only reveal the health of the liver, but are also used to construct multiple scoring systems to evaluate the prognosis of patients. Tools such as the Child-Pugh score, the Model for End-Stage Liver Disease (MELD) score, and the Chronic Liver Failure (CLIF) score provide clinicians with an important basis for evaluating the liver function of ACLF patients and predicting disease progression by combining these biochemical indicators. Inflammation-related indicators such as NLR and other blood biomarkers combining inflammation and liver function, including the new Glasgow prognostic score (neo-GPS), aspartate aminotransferase-lymphocyte ratio index (ALRI), γ-glutamyl transpeptidase-lymphocyte ratio (GLR), and CRP-albumin ratio (CAR) have also been reported as independent prognostic predictors of HCC. In addition, the Barcelona Clinic Liver Cancer (BCLC) scoring and staging system is a clinical staging system for liver cancer, which includes liver function-related indicators and can guide clinical treatment and assist in predicting patient prognosis by evaluating liver function and liver cancer status. The patent document with publication number CN116555416A discloses a predictive model for evaluating the risk of liver failure or regeneration status after liver surgery and its application. It first reveals the application of a marker combination including VEGFA (vascular endothelial growth factor A) and PEDF (human pigment epithelium-derived factor) in the prognosis of liver failure risk or liver regeneration status after liver surgery. Tumor Necrosis Factor receptor superfamily (TNFRSF) plays an important role in processes such as apoptosis and inflammation, and is also involved in cell proliferation and differentiation. Death receptor 5 (DR5, also known as TRAIL-R2, TNFRSF10B) is highly expressed in most liver cancer tissues, but is expressed at a low level in normal tissues and cells. Currently, there is no research on the correlation between DR5 and liver failure based on liver cancer.

[0006] Evidence shows that once patients with unresectable liver cancer develop acute-on-chronic liver failure, their 90-day mortality rate can reach 82.14%. The tools currently provided for assessing liver function and liver cancer status have not been reported in accurately assessing the risk of recent ACLF in patients, especially the risk of ACLF based on HCC. At the same time, there are currently no validated biomarkers to identify whether HCC patients have a clear tendency to liver failure.

[0007] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0008] ACLF is a clinically complex condition, and its pathogenesis involves the interaction of multiple systems and factors, including genetic background, immune status, metabolic abnormalities, infection, and exposure to drugs or toxins. Due to these complex interactions, it is difficult to fully predict the onset and prognosis of ACLF with a single factor or a single biomarker. When diagnosing HBV-ACLF, liver injury indicators such as a significant increase in the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TBIL), as well as a prolonged prothrombin time (PT) are usually relied on. However, in the early stages of acute exacerbation of CHB to ACLF, these indicators may not show obvious changes, making early diagnosis complicated. Some patients may rapidly progress to liver failure even in the absence of cirrhosis, and this phenomenon of liver failure in the early stages of cirrhosis is difficult to detect in time. Therefore, relying solely on traditional biochemical and coagulation indicators, the early and accurate diagnosis of ACLF faces difficulties.

[0009] There are currently some scoring systems for assessing the severity of liver disease, such as Child-Pugh, MELD, NLR, CLIF, CLIF-C ACLFs, and BCLC scores. The Child-Pugh score divides liver reserve function into three levels according to the patient's total score to correspond to liver damage of different severity, but this scoring tool cannot reflect the risk of liver failure caused by the progression of the tumor itself in patients with liver cancer. The MELD score can be used to predict the mortality of non-liver transplant ACLF patients, and can also be used to predict the mortality of patients waiting for liver sources and after liver transplantation. However, this scoring system is also difficult to reflect the risk of liver failure caused by the progression of liver cancer itself. NLR is an inflammatory indicator that reflects the body's immune status and is a relatively simple and fast predictive indicator for the short-term prognosis of HBV-ACLF patients. However, it has certain limitations in grasping the overall liver reserve function, and its predictive value in predicting the risk of liver failure during the development of liver cancer needs further confirmation. The CLIF-CACLFs score is relatively accurate in predicting short-term mortality, but this scoring system has no research related to the prediction of liver failure risk based on liver cancer. BCLC staging has a strong ability to classify and predict the prognosis of liver cancer, but lacks the judgment and guidance significance for predicting the risk of liver failure. In terms of liver failure risk prediction, VEGFA, PEDF, sCD163 and other markers are currently publicly available, but they are mainly aimed at predicting liver failure after liver resection. For liver failure based on unresectable HCC, the research on the prognostic value of these indicators is limited.

[0010] The death of a large number of hepatocytes in the liver tissue of patients with liver failure is the direct cause of liver failure. In the case of ACLF, liver tissue is usually invaded by a large number of inflammatory cells, and the degree and type of inflammation and cell necrosis are crucial to determine the progression and prognosis of the disease. In particular, the internal environment in HCC-ACLF patients is more complex. The rapid growth of the tumor can cause a large number of hepatocytes in the tumor to necrotize, release a large number of endogenous cytokines and inflammatory mediators, and induce or aggravate liver failure. Therefore, in order to more accurately assess the risk of ACLF in patients with liver cancer, it is necessary to introduce new biomarker indicators.

[0011] In view of the shortcomings of the prior art, the first aspect of the present invention provides the use of DR5 in preparing a detection system for the risk prognosis of liver failure. The detection system is preferably a detection reagent, a kit or a detection device. Preferably, the detection system is used to detect the expression level or amount of DR5. Particularly preferably, the detection system is used to detect the expression level or amount of soluble DR5 (sDR5).

[0012] The second aspect of the present invention provides the use of DR5 in preparing a detection system for the risk prognosis of liver failure based on liver cancer. The liver failure based on liver cancer is preferably chronic acute liver failure (HCC-ACLF) based on hepatocellular carcinoma. The liver failure based on liver cancer is preferably chronic acute liver failure based on unresectable hepatocellular carcinoma. The detection system is preferably a detection reagent, a kit or a detection device. Preferably, the detection system is used to detect the expression level or amount of DR5. Particularly preferably, the detection system is used to detect the expression level or amount of soluble DR5 (sDR5) in serum.

[0013] According to a preferred embodiment, the application selected from the following group is performed according to the expression level or amount of DR5:

[0014] Predicting or assessing the risk of liver failure in patients with liver cancer; and / or

[0015] Predict or assess the severity of liver failure due to liver cancer.

[0016] According to a preferred embodiment, according to the expression level or amount of DR5:

[0017] If the expression level or presence of DR5 is greater than 177.78 pg / ml, it indicates that the patient has a risk of developing ACLF within 90 days.

[0018] The third aspect of the present invention provides a kit for assessing the risk of liver failure based on liver cancer, the kit comprising a detection reagent for detecting the expression level or amount of DR5, and instructions for use. The detection reagent can be an immunodetection reagent, a sequencing reagent, or a PCR detection reagent.

[0019] The fourth aspect of the present invention provides a method for detecting the expression level or amount of DR5 using the kit provided in the third aspect of the present invention, comprising the following steps:

[0020] Provide samples for testing;

[0021] Allowing the sample to be tested to react with the detection reagent in the kit;

[0022] adding a label and allowing it to bind to a detection reagent;

[0023] The expression level or amount of DR5 can be quantified or qualified by detecting the signal of the marker.

[0024] The fifth aspect of the present invention provides a detection device for evaluating the risk of liver failure based on liver cancer, which includes the kit, sequencing instrument, chip, probe set, primer probe set or electrophoresis device provided in the third aspect of the present invention.

[0025] The sixth aspect of the present invention provides the use of a combination of DR5, TBIL and INR markers in assessing the risk of liver failure. The risk of liver failure is preferably the risk of liver failure based on liver cancer.

[0026] According to a preferred embodiment, liver failure risk analysis or scoring is performed according to the following risk scoring formula:

[0027] 0.04*DR5+0.012*TBIL+3.006*INR.

[0028] In medicine, the level of TBIL is one of the important indicators for evaluating liver health. INR (International Normalized Ratio) is a medical indicator used to evaluate blood coagulation ability. INR is a standardized ratio calculated by prothrombin time (PT).

[0029] A seventh aspect of the present invention provides a kit or a detection device for assessing the risk of liver failure based on liver cancer, which comprises a detection reagent for detecting the presence of DR5 and TBIL.

[0030] An eighth aspect of the present invention provides a system for assessing the risk of liver failure based on liver cancer, the system comprising a detection unit and a data analysis unit.

[0031] Wherein, the detection unit comprises: a detection reagent for detecting the presence of DR5 and / or TBIL, or a kit or detection device containing the detection reagent;

[0032] The data analysis unit includes: a processing unit for analyzing and processing the detection result of the detection unit.

[0033] According to a preferred embodiment, the processing unit uses the following risk scoring formula to perform risk analysis or scoring of liver failure based on liver cancer:

[0034] 0.04*DR5+0.012*TBIL+3.006*INR.

[0035] Technical effect: Although studies have shown that DR5 is expressed in liver cancer cell lines, and the expression levels of DR5 on the surface of different cells are different, such as 40% in human hepatocellular carcinoma HepG2 cells and 20.3% in human normal liver cells HL-7702. However, the expression of DR5 in the context of liver failure based on liver cancer has not been involved. In patients with early liver cancer, a moderate increase in sDR5 levels may indicate that the tumor load is low and the immune system can still effectively eliminate tumor cells. At this time, sDR5 may reduce the burden on the liver by regulating TRAIL signals, thereby reducing the risk of liver failure. Studies have found that DR5 expression is usually low in the liver tissue of HCC patients. This low expression may be closely related to the occurrence and development of hepatocellular carcinoma. At the same time, for late tumors (such as stage III / IV tumors), the expression of DR5 is significantly lower than that of early tumors (such as stage I / II). The downregulation of DR5 may weaken the response of hepatocytes to apoptosis signals, indicating that liver cancer cells may escape or tolerate TRAIL-induced apoptosis by reducing the expression of DR5, thereby promoting the survival of hepatocytes and the progression of tumors. Although studies have shown that sDR5-Fc protein is safe and effective in treating TRAIL-driven hepatitis and liver failure, the use of DR5 in the risk prognosis assessment of liver failure is not something that can be easily inferred by those skilled in the art based on existing technologies.

[0036] The existing technology mainly focuses on sDR5-Fc protein as a treatment method, which is used to block the TRAIL signaling pathway to prevent hepatocyte apoptosis, thereby treating hepatitis and liver failure. However, there is no disclosure of the application of DR5 in the risk prognosis of liver failure, let alone the application of DR5 in the risk prognosis of liver failure based on liver cancer.

[0037] In terms of functional differences, although DR5 is closely related to the TRAIL signaling pathway, there are significant differences in their mechanisms of action in treatment and prognosis assessment. Therapeutic applications mainly inhibit apoptosis by blocking signaling pathways, while prognosis assessment involves the detection and analysis of DR5 levels or activity in vivo to predict disease progression and risk. It is difficult for those skilled in the art to foresee that the role of DR5 in these two application scenarios is interchangeable.

[0038] In terms of the selectivity of biomarkers, when selecting biomarkers for prognostic assessment, it is often necessary to consider the specific association between the marker and disease progression. The expression of DR5 in the body is regulated by many factors, such as immune response, cell stress, etc., and its level changes may not accurately reflect the risk of liver failure. Biomarkers have limitations or specificity. For example, although alpha-fetoprotein (AFP) has certain specificity in the diagnosis of liver cancer, it is not highly sensitive in assessing liver function status and liver failure risk. AFP levels may be elevated in a variety of liver diseases, leading to misjudgment. Biomarkers of liver failure (such as serum albumin, prothrombin time, etc.) may also be affected by factors other than liver cancer, such as comorbidities, infections, etc., making them inaccurate in assessing the risk of liver cancer patients. Biomarkers of liver cancer are often related to the size or stage of the tumor, while the risk of liver failure may not be related to these factors. Liver failure based on liver cancer has its own uniqueness, involving the interaction of tumor characteristics, liver function damage and multiple physiological factors. Therefore, those skilled in the art generally do not regard DR5 as the preferred marker for prognostic assessment.

[0039] As for the limitations of existing literature, the existing literature mainly reports the therapeutic effects of sDR5-Fc protein, and there is no clear revelation or systematic study on its role in the prognosis of liver failure risk. This means that when facing the clinical application of DR5, the technical personnel in this field are limited to the therapeutic function, and lack the inspiration to expand it to the use of prognostic assessment. The therapeutic function reflects the sensitivity of tumor cells to drugs and the effectiveness of targeted therapy; while prognostic markers are mainly used to predict the natural course of the disease and the survival rate of patients. The therapeutic function is usually related to a specific biological pathway or mechanism. For example, in targeted therapy, it may be directly related to the target of the drug. Markers with prognostic functions may be related to the overall biological characteristics of the disease, inflammatory response, immune status, etc. The biological processes reflected by the two may be completely different. Based on the differences in function, clinical application and biological basis, the application of DR5 cannot replace or derive each other.

[0040] In summary, it is not obvious in the prior art to use DR5 for the risk and prognosis assessment of liver failure, especially for liver failure based on liver cancer. It is difficult for those skilled in the art to predict its use without specific experimental data support or relevant literature disclosure. The application provided by the present invention not only innovatively expands the functional scope of DR5, but also provides a new potential tool for the clinical management of liver failure.

[0041] The main form of DR5 is a type I transmembrane protein, but its extracellular domain can be expressed as a soluble monomer in plasma, thereby producing soluble DR5 (sDR5). It is generally believed that the increase of soluble DR5 in the patient's serum is related to the proliferation of tumor cells and neutrophilia. A significant increase in neutrophils indicates that the body is in an inflammatory state, which is also one of the important characteristics of acute-on-chronic liver failure. Therefore, DR5 is not only highly expressed in most liver cancers, but also an important target for cell necrosis and apoptosis in ACLF, which shows that DR5 can not only be used as an important indicator for evaluating liver failure, but also as an important indicator for evaluating the prognosis of HCC.

[0042] The present invention discovered for the first time that the expression level of DR5 is related to the development of liver failure based on liver cancer, providing a new biomarker in this field. The use of DR5 soluble fragment sDR5 as a new molecular marker for predicting chronic-acute liver failure based on liver cancer has important significance and clinical value. The expression level of DR5 can be used as an indicator for the early diagnosis of liver failure based on liver cancer, which helps to identify high-risk patients in a timely manner. Early diagnosis and treatment of DR5 may help improve the survival rate of patients with liver failure based on liver cancer; the expression of DR5 may also help to assess the severity and prognosis of liver failure in patients with liver cancer. The discovery of the present invention can promote the development of drugs targeting DR5 and provide new drug targets for the treatment of liver failure based on liver cancer. In addition, the study of the association between DR5 and liver failure based on liver cancer may promote cross-collaboration in the fields of hepatology, oncology and biomarker research.

[0043] In terms of correctly identifying the risk of HCC-ACLF, DR5 showed excellent specificity, up to 96.74%, and its sensitivity reached 70.59%, indicating that using DR5 for HCC-ACLF risk prediction can not only ensure the proportion of correct classification, but also improve the accuracy of diagnosis, and has high application value in decision support.

[0044] Furthermore, the three-indicator model of DR5, TBIL and INR can be used as a powerful tool for clinical decision-making to assist doctors in formulating treatment plans based on risk assessment. After obtaining the prediction results of the three-indicator model of DR5, TBIL and INR, clinicians can distinguish the best treatment plans for different types of patients. For example, some patients take supportive treatments for underlying diseases such as liver protection, anti-inflammatory and anti-infection, while others take systemic treatments such as targeted, immune and minimally invasive surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a comparison of the 90-day mortality between ACLF and non-ACLF liver cancer patients;

[0046] Figure 2is the DR5 expression in patients who developed ACLF within 90 days;

[0047] Figure 3 is the comparison result between DR5 and other models as HCC-ACLF risk prediction indicators in the training group;

[0048] Figure 4 It is the specific result of the difference in sensitivity, specificity, and area under the AUC curve between DR5 and other models as HCC-ACLF risk prediction indicators in the training group;

[0049] Figure 5 is the comparison result of DR5 and other models as HCC-ACLF risk prediction indicators in the validation group;

[0050] Figure 6 It is the specific result of the difference in sensitivity, specificity, and area under the AUC curve between DR5 and other models as HCC-ACLF risk prediction indicators in the validation group;

[0051] Figure 7 It is the result of lasso regression screening for the variable of liver failure based on liver cancer;

[0052] Figure 8 It is a decision tree of three variables selected after Lasso-Cox regression;

[0053] Fig. 9 is the AUC curve of the three-index model of DR5, TBIL and INR in the training group and the comparison results with other models (represented by 3Model in the figure);

[0054] Fig.10 It is the comparison result of the sensitivity and specificity of the three-index model of DR5, TBIL and INR in the training group and other models, and the area under the AUC curve of the comparison;

[0055] Fig.11 It is the AUC curve of the three-index model of DR5, TBIL and INR in the validation group and the comparison results with other models;

[0056] Fig.12 It is the comparison result of the sensitivity and specificity of the DR5, TBIL and INR three-index model in the validation group and the difference in the area under the AUC curve between other models. DETAILED DESCRIPTION

[0057] The following is a detailed description with reference to the accompanying drawings.

[0058] Consumables: Human death receptor 5 (DR5) ELISA kit. The experimental methods in the following examples are conventional methods unless otherwise specified. The materials, reagents, etc. used in the following examples can be obtained through commercial channels unless otherwise specified.

[0059] Liver function reserve and synthetic function, as well as hepatocyte apoptosis levels, have a significant impact on the efficacy of systemic treatment for acute-on-chronic liver failure in patients with unresectable HCC. The various existing scoring systems used to assess the severity and prognosis of liver disease in patients have limitations in predicting the risk of ACLF (HCC-ACLF) based on HCC. At the same time, no biomarkers with high sensitivity and specificity that can accurately predict the risk of ACLF based on HCC have been found.

[0060] The reasons for the limited research on the prognostic value of existing scoring systems or biomarkers include: ① Among the Child-Pugh, MELD, NLR, CLIF, CLIF-C ACLFs, BCLC and other scoring systems, only the BCLC staging system is based on liver cancer patients and includes their liver function indicators as prognostic predictors. Other scoring systems are all based on chronic hepatitis and cirrhosis, and have little guiding value for the recent occurrence of liver failure in liver cancer patients; ② The pathogenesis of HCC-ACLF is more complex and there are many factors affecting prognosis. In addition to the liver's reserve capacity and inflammatory environment, there are no verified biomarkers in the existing systems to reflect the potential apoptosis and necrosis tendency of patients' liver cells, so it is impossible to judge the probability of patients developing chronic-on-acute liver failure; ③ At present, the evaluation of liver failure complicated by liver cancer is basically focused on the risk of liver failure after liver resection, and there is a lack of evaluation of liver failure related to unresectable liver cancer; ④ Most (more than 64%) liver cancer patients are in the middle and late stages, and most of them do not have the conditions for surgery. The risk assessment of liver failure will directly determine the doctor's choice of treatment plan for their liver cancer.

[0061] The present invention included 187 patients with unresectable liver cancer (2017), followed up for 2 years, and observed the occurrence of chronic acute liver failure in 187 patients within 90 days. All patients received standardized conventional treatment, including liver protection and enzyme reduction, antiviral (for hepatitis B and C virus infection), anti-infection, correction of water and electrolyte imbalance, supplementation of coagulation factors and plasma, supplementation of albumin, reduction of portal pressure, standardized treatment of tumors (intervention, microwave therapy or palliative treatment), etc.

[0062] Figure 1 Results showed that among 187 patients with unresectable HCC, liver failure occurred within 90 days ( Figure 1 28 patients (represented as ACLF in the literature), 23 of whom died within 90 days; no liver failure ( Figure 1In total, 159 patients (represented as non-ACLF) were enrolled, of whom 5 died. That is, once patients with unresectable liver cancer develop acute-on-chronic liver failure, their 90-day mortality rate can reach 82.14%; while the 90-day survival rate of patients with liver cancer who do not develop ACLF is 96.9%, indicating that the prognosis of liver failure on the basis of liver cancer is extremely poor. It can be seen that the failure to accurately predict the risk of liver failure on the basis of liver cancer can lead to serious consequences, such as increased safety risks for patients due to failure to timely evaluate and intervene, more difficult medical decisions faced by doctors in the absence of accurate evaluation tools, and inaccurate risk assessment affecting the continuity and effectiveness of disease management.

[0063] Therefore, the development of new molecular markers or related prediction systems to predict acute-on-chronic liver failure occurring on the basis of liver cancer is of great significance and clinical value.

[0064] Next, the 187 patients were divided into a training group (109 patients) and a validation group (78 patients) to study the correlation and predictive value of DR5 expression levels and acute-on-chronic liver failure.

[0065] by * P < 0.05 was considered a statistically significant threshold. ** P<0.01, *** P < 0.001, **** P<0.0001.

[0066] Example 1

[0067] This example provides the results of using the single index DR5 for HCC-ACLF risk prediction.

[0068] Figure 2 The DR5 expression of patients who developed ACLF within 90 days, Figure 2 A is the DR5 expression of patients who developed ACLF within 90 days in the training group; Figure 2 B shows the DR5 expression in patients who developed ACLF within 90 days in the validation group. Figure 2In the data, "ACLF-90day" means ACLF occurs within 90 days; "None-ACLF-90day" means ACLF does not occur within 90 days. In the training group, the expression level of DR5 in patients with ACLF within 90 days significantly exceeded the expression level of DR5 in patients without ACLF within 90 days. The results of the validation group were the same as those of the training group, indicating that DR5 has high reliability as a biomarker for predicting liver failure based on liver cancer. The expression level of DR5 in patients with ACLF within 90 days was significantly increased, indicating that the significant expression difference of DR5 provides the possibility for early diagnosis of liver failure based on liver cancer and provides a basis for the development of new clinical detection methods for liver failure. In particular, an expression level of DR5 greater than 177.78pg / ml can be used as a basis for the risk of ACLF. In this embodiment, the expression level of DR5 refers to the content of soluble DR5 in the patient's serum. The cutoff value is 177.78pg / ml.

[0069] Figure 3 The comparison results of DR5 and other models as HCC-ACLF risk prediction indicators in the training group are shown in Figure 2. Figure 3 A is the result of sensitivity and specificity of DR5 in distinguishing the risk of HCC-ACLF; Figure 3 B is the comparison result of sensitivity and specificity between DR5 and other models in distinguishing the risk of HCC-ACLF. Figure 4 The specific results of the differences in sensitivity, specificity, and area under the AUC curve between DR5 and other models as HCC-ACLF risk prediction indicators in the training group.

[0070] Figure 3 and Figure 4 The results showed that the AUC of DR5 was 0.912, the sensitivity was 70.59%, and the specificity was as high as 96.74%, which means that the classification performance of the model was very good and DR5 was very accurate in identifying the risk of HCC-ACLF.

[0071] The AUC of NLR was 0.811, the sensitivity was 88.24%, and the specificity was 72.83%. Compared with DR5, the AUC was lower and the specificity was poorer, and the difference under the curve compared with DR5 was not obvious.

[0072] The AUC of BCLC staging was 0.782, the sensitivity was 88.24%, and the specificity was 66.3%. Compared with DR5, the AUC was lower and the specificity was poorer.

[0073] The AUC of the Child-Pugh score was 0.861, the sensitivity was 76.47%, and the specificity was 86.96%. Compared with DR5, the AUC and specificity were not high, and the difference under the curve compared with DR5 was not obvious.

[0074] The AUC of CLIF score was 0.828, the sensitivity was 94.12%, and the specificity was 60.87%. Compared with DR5, although the sensitivity was higher, the AUC was lower and the specificity was poor. The difference under the curve compared with DR5 was not obvious.

[0075] The MELD score had an AUC of 0.885, a sensitivity of 94.12%, and a specificity of 72.83%. Compared with DR5, although it had a higher sensitivity, it had a lower AUC and a poorer specificity.

[0076] In terms of correctly identifying the risk of HCC-ACLF, DR5 showed excellent specificity, up to 96.74%, and its sensitivity reached 70.59%, indicating that using DR5 for HCC-ACLF risk prediction can not only ensure the proportion of correct classification, but also improve the accuracy of diagnosis, and has high application value in decision support.

[0077] Figure 5 The comparison results of DR5 and other models as HCC-ACLF risk prediction indicators in the validation group, among which, Figure 5 A is the result of sensitivity and specificity of DR5 in distinguishing the risk of HCC-ACLF; Figure 5 B is the comparison result of sensitivity and specificity between DR5 and other models in distinguishing the risk of HCC-ACLF.

[0078] Figure 6 To verify the specific results of the differences in sensitivity, specificity, and area under the AUC curve between DR5 and other models as HCC-ACLF risk prediction indicators in the validation group.

[0079] Figure 5 and Figure 6 In the results, the AUC of DR5 was 0.97, the sensitivity was 81.82%, and the specificity was as high as 95.52%. The performance was good during training and validation, and the results of the validation group were consistent with those of the training group, indicating that the model has consistent performance in different data sets, has good generalization ability, and the prediction results are stable (without excessive noise and details).

[0080] In summary, DR5 can be used as a new molecular marker to predict acute-on-chronic liver failure occurring on the basis of liver cancer.

[0081] Example 2

[0082] This example provides the results of using the three indicators DR5, TBIL and INR for HCC-ACLF risk prediction.

[0083] Figure 7In the study, 187 people were divided into a training group and a validation group. Among the 109 people in the training group, 10 indicators related to chronic acute liver failure were screened out by Lasso regression, including DR5, neutrophil count, total bilirubin, ALP, GGT, INR, tumor diameter greater than 5 cm, number of tumors greater than 3, whether hepatic encephalopathy occurred, and blood creatinine level. After COX multivariate regression screening, three independent prognostic factors for liver failure were obtained, namely DR5 (p = 0.019), TBIL (p = 0.001) and INR (p = 0.002). Even under strong regularization, these three factors still made significant contributions to the model.

[0084] Figure 8 The decision tree of three variables screened after LASSO-COX regression. The results showed that among the three independent influencing factors affecting ACLF in patients with liver cancer obtained by multivariate analysis, DR5 was used in the first-level segmentation of the decision tree (stations 1 and 2), which provided an improvement value of 0.132, which was the highest improvement value among the three variables, indicating that DR5 provided the largest information gain when segmenting the data, which helped to improve the predictive ability of the model. The segmentation point of DR5 was 177.77999, which divided the data into two groups of A≤177.77999 and A>177.77999. The segmentation point (177.77999) provided a clear boundary, dividing the data into two subsets with obvious differences, which helped to identify different risk groups. When DR5 segmented the data, 86.2% of the patients were concentrated in node 1 (station 1), which showed that it could effectively distinguish the patterns in the data set. DR5 successfully clustered data points with similar characteristics at this node, reducing the heterogeneity of the data. Since DR5 performs well in data segmentation, it helps improve the prediction accuracy of the model, especially at early segmentation nodes. As an early segmentation point, DR5 provides direction for subsequent decision making and helps form a clear decision path.

[0085] TBIL was used in the second-level segmentation (stations 3 and 4), with an improvement value of 0.051, which was lower than the improvement value of DR5. When TBIL segmented the data, 83.5% of the patients who needed to be improved in the first node were concentrated in the second node. INR was used in the third-level segmentation (stations 5 and 6), with an improvement value of 0.005, which was the lowest improvement value among the three variables. When INR segmented the data, 71.6% of the patients who needed to be improved in the second node were concentrated in the third node. Although the improvement values ​​of TBIL and INR were low, they still provided additional information to assist in distinguishing subgroups in the data set. The use of TBIL and INR increased the depth of the decision tree, which helped to further refine the classification rules and improve the model's ability to interpret the data. The combined use of DR5, TBIL, and INR helped to stratify the risk groups more accurately. These three variables can assess the risk of liver failure based on liver cancer from different biological pathways, providing a multidimensional risk assessment framework. Each variable is associated with different aspects of liver failure, and combined use can improve the overall prediction accuracy of the model. The decision tree model combining DR5, TBIL and INR can be used as a clinical decision support tool to assist doctors in making more accurate diagnosis and treatment decisions.

[0086] In particular, whether the DR5 expression level is greater than 177.78 pg / ml is the most discriminative feature, i.e., the root node. The next most discriminative child nodes are TBIL and INR. Whether DR5 is greater than 73.15 pg / ml can optimize the decision tree.

[0087] Fig. 9 The AUC curve of the three-index model of DR5, TBIL and INR (represented by 3Model in the figure) in the training group and the comparison results with other models, among which, Fig. 9 A is the AUC curve of the three-index model of DR5, TBIL and INR; Fig. 9 B is the comparison result of AUC curve of DR5, TBIL and INR three-index model with other models.

[0088] Fig.10 The results are the comparison of the sensitivity and specificity of the DR5, TBIL and INR three-index model in the training group and other models, as well as the area under the AUC curve.

[0089] Fig. 9 and Fig.10 In the results, the AUC area of ​​the DR5, TBIL and INR three-indicator model was 0.946, the sensitivity was 94.12%, and the specificity was 86.96%, which was better than the NLR, BCLC staging, Child-Pugh score, CLIF, and MELD models.

[0090] Fig.11The AUC curve of the three-index model of DR5, TBIL and INR (represented by 3Model in the figure) in the validation group and the comparison results with other models, among which, Fig.11 A is the AUC curve of the three-index model of DR5, TBIL and INR; Fig.11 B is the comparison result of AUC curve of DR5, TBIL and INR three-index model with other models.

[0091] Fig.12 To verify the sensitivity and specificity of the DR5, TBIL and INR three-index model in the group and compare the differences in the area under the AUC curve with other models.

[0092] Fig.11 and Fig.12 In the validation group results, the AUC area of ​​the DR5, TBIL and INR three-indicator model was 0.991, the sensitivity was 100%, and the specificity was 95.52%, which was the same as the conclusion of the training group and was superior to the NLR, BCLC staging, Child-Pugh score, CLIF, and MELD models.

[0093] Therefore, the three-indicator model of DR5, TBIL and INR has high accuracy in predicting the risk of liver failure and non-liver failure based on liver cancer, and its risk of missed diagnosis and misdiagnosis is low. At the same time, the results of the validation group and the training group are consistent, indicating that the three-indicator model has good robustness and generalization ability, can maintain performance across different data sets, and can be applied to new and unseen data. The combination of the three indicators provides a more detailed risk stratification, which helps to more accurately assess the risk of liver failure based on liver cancer in individuals.

[0094] The three-indicator model complements each other and may capture different aspects of the development of liver failure, thereby obtaining accurate and individualized risk assessment results. Accurate risk assessment helps optimize the allocation of medical resources and helps improve patient management (improve patient prognosis). The robustness and generalization ability of the three-indicator model enable it to build a bridge between clinical research and practice, helping to transform research results into practical applications. The three-indicator model can be used to track patients' disease progression over a long period of time and adjust treatment strategies in a timely manner. The three-indicator model can continuously learn and improve with new clinical data to maintain the timeliness and accuracy of its clinical decision support.

[0095] Example 3

[0096] The present embodiment provides a system for assessing the risk of liver failure based on liver cancer, the system comprising a detection unit and a data analysis unit, wherein the detection unit comprises: a detection reagent for detecting the presence of DR5 and / or TBIL, or a kit or a detection device containing the detection reagent; wherein the data analysis unit comprises: a processing unit for analyzing and processing the detection results of the detection unit.

[0097] According to a preferred embodiment, the processing unit performs risk analysis or scoring of liver failure based on liver cancer using the following risk scoring formula: 0.04*DR5+0.012*TBIL+3.006*INR.

[0098] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention. The equivalent replacement of the raw materials selected by the present invention, the addition of auxiliary components, the selection of specific methods, etc., also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the description of the present invention and its drawings are illustrative and do not constitute limitations on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The description of the present invention contains multiple inventive concepts, such as "preferably" and "according to a preferred embodiment", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims

1. Application of DR5 in the preparation of a detection system for liver failure risk prognosis.

2. Application of DR5 in the preparation of a liver failure risk prognosis detection system based on liver cancer.

3. The use according to claim 2, characterized in that: According to the expression level or amount of DR5, an application selected from the following group is performed: predicting or evaluating the risk of occurrence of liver failure based on liver cancer; and / or predicting or evaluating the severity of liver failure based on liver cancer.

4. The use according to claim 2 or 3, characterized in that: The liver failure due to liver cancer is preferably acute-on-chronic liver failure due to hepatocellular carcinoma.

5. The use according to claim 2 or 3, characterized in that: The detection system is used to detect the expression level or amount of soluble DR5 in serum.

6. A kit for assessing the risk of liver failure based on liver cancer, characterized in that: The kit comprises a detection reagent for detecting the expression level or amount of DR5, and instructions for use.

7. A method for detecting the expression level or amount of DR5 using the kit according to claim 6, characterized in that: The following steps are involved: Provide samples for testing; Allowing the sample to be tested to react with the detection reagent in the kit; adding a label and allowing it to bind to a detection reagent; The expression level or amount of DR5 can be quantified or qualified by detecting the signal of the marker.

8. A detection device for evaluating the risk of liver failure based on liver cancer, the detection device comprising the kit, sequencing instrument, chip, probe set, primer probe set or electrophoresis device according to claim 6.

9. A kit or detection device for assessing the risk of liver failure based on liver cancer, characterized in that: Includes detection reagents for detecting the presence of DR5 and TBIL.

10. A system for assessing the risk of liver failure based on liver cancer, characterized in that: The system comprises a detection unit and a data analysis unit, Wherein, the detection unit comprises: a detection reagent for detecting the presence of DR5 and / or TBIL, or a kit or detection device containing the detection reagent; The data analysis unit includes: a processing unit for analyzing and processing the detection result of the detection unit.

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