Use of cfDNA concentration to prepare an alf early prediction product, construction method and system

Abstract

The application provides a use, a construction method and a system for preparing an ALF early prediction product based on cfDNA concentration, belongs to the technical field of medical detection, and the ALF early prediction product is used for early prediction of severe liver injury or rapid clinical deterioration of acute liver failure of a to-be-detected person without previous liver disease. Only the cfDNA concentration or a few more indexes need to be detected to early warn the liver failure, without puncture and complex instruments and equipment and experimental schemes, and the operation is simple and fast, low in cost, short in detection period and high in universality.

Description

Technical Field

[0001] The present invention belongs to the field of medical detection technology, and specifically relates to the use, construction method and system of preparing ALF early prediction products based on cfDNA concentration. Background Art

[0002] Liver failure is a severe form of liver damage caused by multiple factors, leading to severe impairment or decompensation of the synthesis, detoxification, metabolism, and biotransformation functions. This syndrome manifests as a cluster of clinical symptoms, including jaundice, coagulopathy, hepatorenal syndrome, hepatic encephalopathy, and ascites. Based on medical history, onset characteristics, and rate of disease progression, liver failure in my country is classified into four categories: acute liver failure (ALF), subacute liver failure (SALF), acute-on-chronic liver failure (ACLF), and chronic liver failure (CLF). ALF refers to severe liver damage and rapid clinical deterioration in patients without prior liver disease.

[0003] It is a clinical consensus that liver failure progresses rapidly, is difficult to treat, and has a high mortality rate. Once liver failure occurs, existing comprehensive medical treatment and artificial liver therapy have limited effectiveness. Liver transplantation is currently the most effective treatment, but it is limited by many factors, such as the scarcity of donor livers and high medical costs. If the risk of liver failure can be predicted early and timely intervention can be carried out before the onset of liver failure, thus preventing the progression of the disease, it will undoubtedly effectively improve the patient's prognosis and reduce the patient's financial burden. At present, scholars at home and abroad have conducted research on early warning models related to ACLF and have achieved good predictive capabilities. However, there is little research on ALF and related early warning models at home and abroad, and most research focuses on ACLF.

[0004] Therefore, it is necessary to develop a new ALF early warning method that is simple to operate, cost-controlled, highly accurate, and universal, so as to improve the prevention and treatment level of liver failure in my country and thereby increase the survival rate of patients. Summary of the Invention

[0005] To address these issues, the present invention provides uses, methods, and systems for preparing an early prediction product for ALF based on cfDNA concentration. Simply measuring cfDNA concentration or a small increase in an indicator can provide an early warning of liver failure, eliminating the need for biopsy, complex equipment, or experimental protocols. The system is simple, fast, low-cost, and has a short detection cycle and broad applicability.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] The purpose of preparing an ALF early prediction product based on cfDNA concentration, wherein the ALF early prediction product is used to make early predictions of severe liver damage or acute liver failure with rapid clinical deterioration in subjects without previous liver disease.

[0008] Furthermore, the cfDNA is extracted from the body fluid of the subject, and the body fluid includes urine, saliva, cerebrospinal fluid, blood, plasma or serum.

[0009] Furthermore, the first optimal threshold for distinguishing between acute liver failure and healthy individuals is determined by drawing ROC curves of acute liver failure patients and healthy people in the training set, and using the Youden index as the first optimal threshold for distinguishing between acute liver failure patients and healthy people.

[0010] A kit for early prediction of ALF, comprising: an alpha-fetoprotein detection reagent and a cfDNA extraction reagent; based on the concentrations of the alpha-fetoprotein and cfDNA, early prediction of severe liver damage and acute liver failure with rapid clinical deterioration is performed in subjects without previous liver disease.

[0011] Furthermore, the cfDNA is extracted from the body fluid of the subject, and the body fluid includes urine, saliva, cerebrospinal fluid, blood, plasma or serum.

[0012] A method for constructing an early prediction model for ALF comprises: determining a training set and a validation set, both of which include patients with acute liver failure and healthy subjects; collecting cfDNA concentration, alpha-fetoprotein concentration, age, and gender information of samples in the training set and the validation set; constructing a model based on the cfDNA concentration or the cfDNA concentration, alpha-fetoprotein concentration, age, and gender of the training set to obtain an optimal threshold; and validating the model and the optimal threshold in the validation set.

[0013] Furthermore, the cfDNA is extracted from the body fluids of the subject, which include urine, saliva, cerebrospinal fluid, blood, plasma or serum; and the alpha-fetoprotein concentration is detected through blood, plasma or serum.

[0014] Furthermore, when the model is constructed using cfDNA concentration, when the cfDNA concentration is not less than the first optimal threshold, the result is judged to be positive, otherwise it is negative; the first optimal threshold is determined by drawing the ROC curve of patients with acute liver failure and healthy people in the training set, and using the Youden index as the first optimal threshold to distinguish patients with acute liver failure from healthy people.

[0015] Furthermore, when constructing a model using cfDNA concentration, alpha-fetoprotein concentration, age, and sex, sex was standardized by assigning a value of 1 to males and 0 to females; the model was:

[0016] ALFscreen = -6.04297-0.01424Age+0.46453Gender+0.11339AFP+6.55116Qubit; when the ALFscreen value is not less than the second optimal threshold, the result is judged to be positive, otherwise it is negative; the second optimal threshold is determined by drawing the ROC curve of patients with acute liver failure and healthy people in the training set, and using the Youden index as the second optimal threshold for distinguishing patients with acute liver failure from healthy people.

[0017] An ALF early prediction model system comprises: a processor and a memory, wherein the processor can construct an ALF early prediction model according to the construction method described in any one of claims 6 to 9 and perform sample information processing; the sample information processing comprises: inputting the cfDNA concentration or cfDNA concentration, alpha-fetoprotein concentration, age, and gender information of the individual to be tested into the ALF early prediction model in the processor; the processor evaluating the risk assessment result of the sample to be tested based on the ALF early prediction model; and the memory is used to store the risk assessment result.

[0018] The technical solution provided by the present invention has the following beneficial effects: the existing technology lacks early warning markers for ALF. The present invention proposes corresponding biomarkers and a corresponding model for early prediction of ALF to achieve early prediction of ALT; secondly, the ALT early prediction model proposed in this application has high sensitivity and specificity, with a sensitivity of more than 73% and a specificity of 100%; finally, only the detection of cfDNA concentration or a small increase in indicators is required to provide early warning of liver failure, without the need for puncture and complex instruments and equipment and experimental protocols. The operation is simple and fast, the cost is low, the detection cycle is short, and the universality is strong. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is the ROC curve for early prediction of ALF based on cfDNA concentration provided in Example 1 of the present invention;

[0021] Figure 2 This is the ROC curve for early prediction of ALF based on the ALFscreen model provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described in detail below by way of specific embodiments. However, it will be understood by those skilled in the art that the following examples are intended only to illustrate the present invention and should not be construed as limiting the scope of the present invention. Where specific techniques or conditions are not specified in the examples, the techniques or conditions described in the literature in this field or the product instructions are used. Where the manufacturer of the reagents or instruments used is not specified, they are all conventional products that can be obtained commercially.

[0023] As used herein, the words "comprises," "includes," "has," or any other variations thereof are intended to encompass a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises listed elements is not necessarily limited to those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. In addition to the specific methods, devices, and materials used in the examples, any prior art methods, devices, and materials similar or equivalent to those in the examples may be used to implement the present invention, based on the knowledge of those skilled in the art and the disclosure of this invention.

[0025] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt conventional techniques in molecular biology, immunology, laboratory science, gene sequencing technology, bioinformatics technology, and related fields in the art.

[0026] The advance prediction time for early prediction of acute liver failure in the present invention is 1-2 weeks.

[0027] An embodiment of the present invention provides the use of a product for the early prediction of ALF based on cfDNA concentration, wherein the product is used for the early prediction of severe liver damage or acute liver failure with rapid clinical deterioration in subjects without prior liver disease.

[0028] After extensive and in-depth research, the present invention found that the mechanisms leading to ALF can be summarized into two categories: the first is that pathogens or toxic substances directly damage organelles or trigger cell cascade pathways, disrupting cellular homeostasis. The second is that the immune response (including innate and adaptive) ultimately converges on the liver cell death pathway, including apoptosis, autophagy, and necrosis (also known as programmed necrosis), leading to immune-mediated liver damage. Regardless of which of the above mechanisms is used, a large number of liver cells will undergo apoptosis and necrosis, and the liver is an organ closely related to the blood. The large amount of cfDNA released by liver cell apoptosis or necrosis will quickly enter the blood, causing the cfDNA concentration in the blood to increase sharply, which is very suitable for blood testing using liquid biopsy technology. The present invention uses the concentration of cfDNA for early prediction of ALT, with high sensitivity and specificity.

[0029] Specifically, the cfDNA is extracted from the body fluid of the subject, and the body fluid includes urine, saliva, cerebrospinal fluid, blood, plasma or serum.

[0030] Specifically, the first optimal threshold for distinguishing between acute liver failure and healthy individuals is determined by drawing ROC curves of acute liver failure patients and healthy people in the training set, and using the Youden index as the first optimal threshold for distinguishing between acute liver failure patients and healthy people. Preferably, the first optimal threshold of the present invention is 0.823.

[0031] An embodiment of the present invention also provides a kit for early prediction of ALF, comprising: an alpha-fetoprotein detection reagent and a cfDNA extraction reagent; based on the concentrations of the alpha-fetoprotein and cfDNA, early prediction of severe liver damage and acute liver failure with rapid clinical deterioration is performed in subjects without previous liver disease.

[0032] Alpha-fetoprotein, or AFP, is a special protein found in the blood during the embryonic period. It is synthesized only in the fetal liver cells and yolk sac. It is particularly high between 10 and 13 weeks of gestation, when the fetus's protein content is highest, reaching 300-500 mg per 100 ml of blood. It then gradually decreases. From birth to one year old, most of the AFP is replaced by albumin, and the AFP level drops to adult levels (<20 ng / ml). Because its concentration is high in fetal blood and very low in the serum of healthy adults, its full name is alpha-fetoprotein. The onset of ALT is often accompanied by the death and regeneration of a large number of liver cells. These newly formed liver cells regain the ability to synthesize AFP, resulting in a high AFP concentration in the blood reaching 2000 ng / ml. This is how AFP testing can aid in the early prediction of ALT.

[0033] The cfDNA is extracted from the body fluid of the subject, and the body fluid includes urine, saliva, cerebrospinal fluid, blood, plasma or serum.

[0034] The extraction reagent adopts an existing commercially available product.

[0035] An embodiment of the present invention further provides a method for constructing an ALF early prediction model, comprising:

[0036] S1 determines a training set and a test set, both of which include patients with acute liver failure and healthy subjects.

[0037] Preferably, the training set and the test set are randomly divided in a ratio of 2:1.

[0038] S2 collects cfDNA concentration, alpha-fetoprotein concentration, age, and gender information of samples in the training set and test set.

[0039] The cfDNA is extracted from a subject's body fluids, including urine, saliva, cerebrospinal fluid, blood, plasma, or serum. The alpha-fetoprotein concentration is measured in blood, plasma, or serum.

[0040] AFP detection: Plasma was separated and 750 μl of plasma was pipetted into a 1.5 ml EP tube. A chemiluminescent immunoassay of the AFP target protein molecule was performed using an AFP assay kit (chemiluminescent microparticle immunoassay, CMIA) on an ARCHITECT i2000SR fully automated immunoassay instrument (Abbott, USA).

[0041] cfDNA extraction: using the Apostle MiniMax from Nanke Zhengtu TM The cell-free DNA (cfDNA) isolation and enrichment kit (Cat#: A17622-10, Version: P.27) was used to extract free cfDNA from plasma and collect it into a 1.5 mL centrifuge tube.

[0042] cfDNA concentration determination: The extracted cfDNA was measured using Qubit 4.0 (Life Technologies, USA) in a dark environment. TM dsDNA HS buffer + 1μL Qubit TMPrepare the N+1 system mixture according to the ratio of dsDNAHSreagent. Select 5mL, 15mL or 50mL centrifuge tubes according to the volume of the mixture, add the above corresponding reagents to the centrifuge tube, mix it upside down 4-5 times, vortex mix it for 3-5s, and centrifuge it for 3-5s. Add 198μL of the prepared mixture to each 0.5mL PCR thin-walled tube (flat cover), vortex mix the DNA product collected in step 1 in the 1.5mL centrifuge tube for 10s and then centrifuge it. After checking the sample number, take 2μL of DNA and add it to the 198μL reaction system in turn, vortex mix it for 3-5s, centrifuge it for 3-5s, and measure the DNA concentration after reacting in the dark for 2min.

[0043] S3 built a model based on the training set's cfDNA concentration or cfDNA concentration, alpha-fetoprotein concentration, age, and sex. Receiver operating characteristic (ROC) curves were plotted for patients with acute liver failure and healthy controls in the training set, and the Youden index was used as the optimal threshold for distinguishing patients with acute liver failure from healthy controls.

[0044] S4 performs validation in a validation set based on the model and the optimal threshold.

[0045] When the model is constructed using cfDNA concentration, if the cfDNA concentration is not less than the first optimal threshold, the result is judged to be positive, otherwise it is negative; the first optimal threshold is determined by drawing the ROC curve of patients with acute liver failure and healthy people in the training set, and using the Youden index as the first optimal threshold to distinguish between patients with acute liver failure and healthy people.

[0046] When the model is constructed using cfDNA concentration, alpha-fetoprotein concentration, age, and sex, and sex is standardized by assigning a value of 1 to males and 0 to females, the model is:

[0047] ALFscreen=-6.04297-0.01424Age+0.46453Gender+0.11339AFP+6.55116Qubit;

[0048] When the ALFscreen value is not less than the second optimal threshold, the result is considered positive, otherwise it is negative;

[0049] The second optimal threshold is determined by drawing ROC curves of patients with acute liver failure and healthy people in the training set, and using the Youden index as the second optimal threshold for distinguishing patients with acute liver failure from healthy people. Preferably, the value of the second optimal threshold is 0.043.

[0050] The two models provided by the present invention can meet a sensitivity greater than 73% and a specificity of 100%.

[0051] An embodiment of the present invention also provides an ALF early prediction model system, comprising: a processor and a memory, wherein the processor can construct an ALF early prediction model according to the above-mentioned construction method and perform sample information processing; the sample information processing comprises: inputting the cfDNA concentration or cfDNA concentration, alpha-fetoprotein concentration, age and gender information of the individual to be tested into the ALF early prediction model in the processor; the processor evaluates the risk assessment result of the sample to be tested based on the ALF early prediction model; and the memory is used to store the risk assessment result.

[0052] The electronic device of this embodiment may include a memory, a processor, a bus, and a communication interface. The memory, the processor, and the communication interface are electrically connected to each other directly or indirectly to achieve data interaction and transmission.

[0053] In this embodiment, the memory can be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0054] In this embodiment, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FGPA), or other programmable logic devices, discrete gate or crystal logic devices, or discrete hardware components.

[0055] In practical applications, the ALF early prediction model system can be an electronic device, such as a server, a cloud platform, a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (PDA), a wearable electronic device, a virtual reality device, etc. Therefore, the embodiments of the present application do not limit the type of electronic device.

[0056] In this embodiment, a device for detecting cfDNA and alpha-fetoprotein is also provided, which can simultaneously detect the concentrations of cfDNA and alpha-fetoprotein, including sample acquisition, a sample plasma concentration detection device, and a real-time display device for sample plasma cfDNA and alpha-fetoprotein concentrations.

[0057] In this embodiment, a computer-readable medium is also provided, on which a computer program is stored. When executed by a processor, the computer program can implement the construction of a random forest model. The computer-readable medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory, a random access memory, a magnetic disk, a floppy disk, or an optical disk.

[0058] Example 1

[0059] Based on the application of cfDNA concentration in the early prediction of ALF, the present invention tested plasma samples collected from 53 clinical patients with acute liver failure and 60 healthy subjects. The test included:

[0060] 1. The test samples were extracted and purified from the free cfDNA in the peripheral blood using the high-efficiency free DNA enrichment and separation kit of Nanke Zhengtu Company.

[0061] 2. Perform Qubit assay on the extracted cfDNA as described in the instructions.

[0062] 3. The test samples are randomly divided into training set and validation set in a ratio of approximately 2:1. The sample conditions of the training set and validation set are shown in Tables 1 and 2.

[0063] Table 1: Training set and validation set division

[0064] - training set Validation set total Acute liver failure 34 19 53 Healthy people 42 18 60 total 76 37 113

[0065] The ROC curve was drawn using the samples of the training set to determine the optimal cut-off value of cfDNA concentration. Figure 1As shown in the figure, the final AUC (area under the curve) of cfDNA concentration was 0.964, and the optimal cutoff was 0.823. When the cfDNA concentration was not less than 0.823 ng / μL, the result was judged to be positive.

[0066] When analyzing performance, the following formula is used:

[0067] Sensitivity (%) = true positive / [true positive + false negative]

[0068] Specificity (%) = true negative / [true negative + false positive]

[0069] Therefore, in the training set, when cut off = 0.823, the sensitivity is 30 / (30+4) = 88.2% and the specificity is 41 / (41+1) = 97.6%.

[0070] 4. Performance verification of cfDNA concentration using the validation set data yielded an AUC of 0.918. With a cutoff of 0.823, the sensitivity was 14 / (14+5) = 73.7%, and the specificity was 18 / (18+0) = 100%.

[0071] Table 2 Training set and validation set samples

[0072]

[0073]

[0074]

[0075]

[0076] Example 2

[0077] The present invention establishes a multi-index ALT early prediction model and applies it to early ALT prediction. The model tests plasma samples collected from 53 patients with clinical acute liver failure and 60 healthy controls. The model includes:

[0078] 1. The division of samples, training sets, and validation sets in Example 2 is the same as that in Example 1. At the same time, the detection data of age (Age), gender (Gender), and alpha-fetoprotein (AFP) are included, and gender is assigned a value of 1 for males and 0 for females for standardization. The training set and validation set samples are shown in Table 3.

[0079] 2. Use the training set data to build a logistic regression model. Use the logistic regression model package {glmnet} of R version 4.3.2 to develop this logistic regression model ALFscreen. The model formula is:

[0080] ALFscreen=-6.04297-0.01424Age+0.46453Gender+0.11339AFP+6.55116Qubit

[0081] like Figure 2 As shown, the model's AUC (area under the curve) is 0.980, with an optimal cutoff of 0.043. A model score of 0.043 or higher is considered positive, while a score below 0.043 is considered negative. The model's sensitivity for acute liver failure is 32 / (32 + 2) = 94.1%, and its specificity is 42 / (42 + 0) = 100%.

[0082] 3. The performance of the ALFscreen logistic regression model was verified using data from the validation set. In the validation set, the ALFscreen model achieved an AUC of 0.974, a sensitivity of 16 / (16+3) = 84.2%, and a specificity of 18 / (18+0) = 100% for the comprehensive assessment of acute liver failure.

[0083] Table 3 Training set and validation set samples

[0084]

[0085]

[0086]

[0087]

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An ALF early prediction model system, characterized by: include: processor and memory, The processor can process sample information according to the ALF early prediction model; The sample information processing includes: inputting the cfDNA concentration, alpha-fetoprotein concentration, age and gender information of the individual to be tested into the ALF early prediction model in the processor; the processor evaluating the risk assessment result of the sample to be tested based on the ALF early prediction model; The memory is used to store the risk assessment result; The ALF early prediction model is composed of cfDNA concentration, alpha-fetoprotein concentration, age and gender; Gender is standardized by assigning 1 to male and 0 to female; The ALF early prediction model is: ALFscreen=-6.04297-0.01424Age+0.46453Gender+0.11339AFP+6.55116Qubit; When the ALFscreen value is not less than the second optimal threshold, the result is considered positive, otherwise it is negative; The second optimal threshold is determined by drawing ROC curves of patients with acute liver failure and healthy people in the training set, and using the Youden index as the second optimal threshold for distinguishing patients with acute liver failure from healthy people.

2. The ALF early prediction model system according to claim 1, characterized in that: The cfDNA is extracted from the body fluids of the subject, which include urine, saliva, cerebrospinal fluid, blood, plasma or serum; the alpha-fetoprotein concentration is detected by blood, plasma or serum.

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