Biomarker for auxiliary diagnosis of hepatic diseases and application

Abstract

The invention belongs to the technical field of liver disease diagnosis, and particularly relates to a biomarker for auxiliary diagnosis of liver diseases and application, the biomarker comprises serum phosphatidylcholine, and further comprises any one or two of alpha fetoprotein and chitosanase 3-like protein 1. As a newly discovered liver disease diagnosis index, the PC provided by the invention has the highest predictive ability in hepatitis and higher predictive ability in liver cirrhosis and liver cancer, can improve the predictive ability of other indexes on liver diseases, and has very high clinical value.

Description

Technical Field

[0001] This invention belongs to the field of liver disease diagnosis technology, specifically relating to a biomarker for assisting in the diagnosis of liver diseases and its application. Background Technology

[0002] Liver diseases include hepatitis, cirrhosis, and liver cancer, making screening and diagnosis crucial. Liver cancer is one of the most common malignant tumors of the digestive tract, with high global incidence and mortality rates. Hepatocellular carcinoma (HCC) is the dominant type of liver cancer, accounting for approximately 75%–80% of all liver cancers. HCC incidence is primarily associated with hepatitis B and C virus infection, alcohol consumption, aflatoxin exposure, and lipid metabolism abnormalities, with mortality increasing with age. The continuously rising incidence and mortality rates of liver cancer represent a significant global public health burden. Due to the lack of specific symptoms, most HCC patients are diagnosed at an intermediate or advanced stage, making radical surgical resection unsuitable. Therefore, early screening of key populations for HCC is essential. Currently, the main molecular marker for HCC detection is serum alpha-fetoprotein (AFP); however, AFP is not ideal for diagnosing early-stage hepatitis.

[0003] Therefore, it is necessary to study a biomarker that can help diagnose hepatitis, cirrhosis and liver cancer. Summary of the Invention

[0004] The purpose of this invention is to provide a biomarker for assisting in the diagnosis of liver diseases and its application, so as to solve the above-mentioned technical problems.

[0005] To achieve the above-mentioned technical objectives, the technical solution of the present invention is as follows: A biomarker for the auxiliary diagnosis of liver disease includes serum phosphatidylcholine (PC), and also includes any one or two of alpha-fetoprotein and chitinase 3-like protein 1 (CHI3L1).

[0006] Preferably, the liver disease is one or more of hepatitis, cirrhosis, and liver cancer.

[0007] The purpose of using biomarkers to assist in the diagnosis of liver diseases as targets for the preparation of detection and screening of drugs for liver diseases, wherein the liver diseases are one or more of hepatitis, cirrhosis and liver cancer.

[0008] A kit for detecting liver diseases, the kit comprising reagents for detecting the aforementioned biomarkers used to aid in the diagnosis of liver diseases.

[0009] Due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows: The biomarkers for auxiliary diagnosis of liver diseases provided by the present invention include serum phosphatidylcholine, and also include any one or two of alpha-fetoprotein and chitosan polysaccharide enzyme 3-like protein 1. As biomarkers, they can be used for serological examination, which helps to assist in the detection and diagnosis of liver diseases, thereby enabling the implementation of corresponding treatment measures as early as possible.

[0010] The biomarkers for auxiliary diagnosis of liver diseases provided by this invention have high sensitivity and specificity for patients with liver diseases. In experiments, the sensitivity and specificity for hepatitis patients reached 95.24% and 95.24%, respectively; for cirrhosis patients, the sensitivity and specificity reached 80.95% and 90.48%, respectively; and for liver cancer patients, the sensitivity and specificity reached 95.24% and 95.24%, respectively.

[0011] The biomarkers for auxiliary diagnosis of liver diseases provided in this invention, including serum phosphatidylcholine, combined with AFP and CHI3L1, improve the specificity and sensitivity of disease diagnosis, further enhance the accuracy of liver disease diagnosis, and provide a scientific basis for the early clinical diagnosis and assessment of liver cancer patients. Attached Figure Description

[0012] Figure 1 This is a bar chart showing serum PC levels in the healthy control group, hepatitis group, cirrhosis group, and liver cancer group. Figure 2 These are the ROC curves of serum PC in the hepatitis group, cirrhosis group, and liver cancer group; Figure 3 This is the ROC curve of serum AFP in the hepatitis group; Figure 4 This is the ROC curve of serum CHI3L1 in the hepatitis group; Figure 5 This is the ROC curve of serum PC in the hepatitis group; Figure 6 This is the ROC curve of combined detection of serum AFP and PC in the hepatitis group; Figure 7 This is the ROC curve of combined detection of serum CHI3L1 and PC in the hepatitis group; Figure 8 The ROC curve is the result of combined detection of serum AFP and CHI3L1 in the hepatitis group. Figure 9 The ROC curve is obtained from the combined detection of serum AFP, CHI3L1, and PC in the hepatitis group. Figure 10 This is the ROC curve of serum AFP in the cirrhosis group; Figure 11 This is the ROC curve of serum CHI3L1 in the cirrhosis group; Figure 12This is the ROC curve of serum PC in the cirrhosis group; Figure 13 This is the ROC curve of combined detection of serum AFP and PC in the cirrhosis group; Figure 14 This is the ROC curve of combined detection of serum CHI3L1 and PC in the cirrhosis group; Figure 15 The ROC curves for combined detection of serum AFP and CHI3L1 in the cirrhosis group are shown. Figure 16 The ROC curves for combined detection of serum AFP, CHI3L1, and PC in the cirrhosis group are shown. Figure 17 This is the ROC curve of serum AFP in the liver cancer group; Figure 18 This is the ROC curve of serum CHI3L1 in the liver cancer group; Figure 19 This is the ROC curve of serum PC in the liver cancer group; Figure 20 This is the ROC curve of combined detection of serum AFP and PC in the liver cancer group; Figure 21 The ROC curve is obtained by combined detection of serum CHI3L1 and PC in the liver cancer group; Figure 22 The ROC curves for combined detection of serum AFP and CHI3L1 in the liver cancer group are shown. Figure 23 This is the ROC curve of serum AFP, CHI3L1 and PC combined detection in the liver cancer group. Detailed Implementation

[0013] The technical solution of the present invention will be clearly and completely described below with reference to specific embodiments. However, those skilled in the art will understand that the embodiments described below are some embodiments of the present invention, but not all embodiments, and are only used to illustrate the present invention, and should not be regarded as limiting the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall be followed. Where the manufacturers of reagents or instruments are not specified, they are all conventional products that can be purchased commercially.

[0014] This invention discloses a biomarker for assisting in the diagnosis of liver diseases, comprising serum phosphatidylcholine, and also comprising any one or two of alpha-fetoprotein and chitosanase 3-like protein 1.

[0015] The diagnosed liver diseases included hepatitis, cirrhosis, and liver cancer.

[0016] Used as a biomarker to aid in the diagnosis of liver diseases, and as a target for the preparation and screening of drugs for the detection and screening of hepatitis, cirrhosis and liver cancer.

[0017] A kit for detecting liver diseases, comprising reagents for detecting the aforementioned biomarkers used to aid in the diagnosis of liver diseases.

[0018] This invention discloses the application of serum phosphatidylcholine (PC) as a biomarker for liver diseases, belonging to the field of biomedical detection. By detecting serum PC levels in healthy controls, hepatitis groups, cirrhosis groups, and liver cancer groups, this invention clarifies the clinical value of serum PC in patients with liver diseases, proposes that serum PC be used as a biomarker for the diagnosis and prognostic assessment of liver diseases, and provides a new auxiliary detection method for clinical practice, possessing significant clinical application value.

[0019] See bar charts for serum PC levels in healthy controls, hepatitis, cirrhosis, and liver cancer patients. Figure 1 ROC curves of serum PC in patients with hepatitis, cirrhosis, and liver cancer are shown in [the table]. Figure 2 .

[0020] The technical solution described above is as follows: I. Serum samples were collected from 21 healthy individuals undergoing physical examinations, 21 patients with hepatitis, 21 patients with cirrhosis, and 21 patients with liver cancer. AFP results were retrieved and collected, and serum CHI3L1 and PC levels were measured.

[0021] 2. Perform ROC curve plots for single-indicator and multi-indicator combined diagnosis to clarify the specificity and sensitivity of AFP, CHI3L1, PC, AFP+CHI3L1, AFP+PC, PC+CHI3L1, and AFP+CHI3L1+PC in diagnosing liver diseases.

[0022] Exemplary embodiments will be described in detail below. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of methods consistent with some aspects of this disclosure. Unless otherwise specified, the experimental methods in the following embodiments are conventional methods or are performed according to the conditions recommended by the manufacturer. Unless otherwise specified, the materials, reagents, etc. used in the following embodiments are commercially available.

[0023] I. Serum samples were collected from 21 healthy individuals undergoing physical examinations as the healthy control group, and serum samples were collected from 21 patients with hepatitis, cirrhosis, and liver cancer as the hepatitis group, cirrhosis group, and liver cancer group, respectively.

[0024] After collecting blood using a yellow procoagulant tube, centrifuge at 3000 rpm for 10 minutes to separate serum and red blood cells. Collect the serum for later use. Note that the following samples cannot be used: 1. Samples containing hemolysis or lipemia; 2. Samples showing signs of microbial contamination; 3. Samples of thermal fire extinguishing; 4. Samples containing clumps or flocculent material; 5. Samples that have undergone more than 3 freeze-thaw cycles.

[0025] II. Collect serum AFP results from the healthy control group, hepatitis group, cirrhosis group, and liver cancer group, and detect serum CHI3L1 and PC.

[0026] The serum AFP results of the healthy control group, hepatitis group, cirrhosis group, and liver cancer group are shown in Table 1.

[0027] Table 1. Serum APF values ​​in the healthy control group, hepatitis group, cirrhosis group, and liver cancer group.

[0028] Assay for serum CHI3L1 detection: The CHI3L1 detection kit (chemiluminescence method) was purchased from Hunan Yahuilong Biotechnology Co., Ltd., REF C86111, and the detection instrument was an iFlash3000-H. The kit uses a sandwich immunoassay with direct chemiluminescence technology. CHI3L1 in the sample reacts with CHI3L1 antibody coated on paramagnetic microparticles and acridine-labeled CHI3L1 antibody conjugates to form a sandwich (antibody-antigen-antibody) complex. Under the influence of a magnetic field, the magnetic microparticles are adsorbed onto the reaction tube wall, and unbound substances are washed away with washing solution. Pre-excitation and excitation solutions are added to the reaction complex, and the chemiluminescence reaction is measured by relative luminescence intensity. The amount of CHI3L1 in the sample is positively correlated with the relative luminescence intensity measured by the optical system of the instrument.

[0029] The kit includes: Reagent 1 (R1, 2 bottles × 3.5mL): Magnetic microparticle reagent coated with chitosanase 3-like protein 1 antibody, containing Tris buffer and preservative; Reagent 2 (R2, 2 bottles × 4.0 mL): Chitosan polysaccharide enzyme 3-like protein 1 antibody acridine-labeled conjugate reagent, containing PBS buffer and preservative; Reagent 3 (R3, 2 vials × 10.0 mL): Sample diluent containing Tris buffer and preservative; Calibrator 1 (CAL1, 1 vial × 1.0 mL): contains CHI3L1 (concentration of 0.18 ng / mL), protein stabilizer, Tris buffer, and preservative; Calibrator 2 (CAL2, 1 vial × 1.0 mL): Contains CHI3L1 (concentration 72.78 ng / mL), protein stabilizer, Tris buffer, and preservative; Calibrator 3 (CAL3, 1 vial × 1.0 mL): contains CHI3L1 (concentration of 971.11 ng / mL), protein stabilizer, Tris buffer, and preservative; The specific operating steps are as follows: 1. Equilibrate all components of the kit to room temperature.

[0030] 2. Load the CHI3L1 reagent bottle. R1 must be fully suspended before placing the bottle on the instrument. Before testing, the instrument will automatically resuspend the magnetic particles to ensure they are well mixed.

[0031] 3. Scan the barcode on the reagent bottle or manually enter the serial number to read the relevant test parameters.

[0032] 4. Place calibrators CAL1, CAL2, and CAL3 in the specimen testing area of ​​the instrument. The calibrators should only be kept open during calibration. The recommended ambient temperature for testing is 10℃-30℃.

[0033] 5. Apply for calibration, start testing, confirm calibration results, and it will show as successful.

[0034] 6. Apply for quality control, start testing, check the quality control data, which shows as under control.

[0035] 7. Perform sample setup, select the application menu, enter the sample number, rack number and cup position, select the test item CHI3L1, select the sample quantity, and select save.

[0036] 8. Load the sample, place the sample rack on the sample holder, and then place the sample rack on the sample tray of the instrument.

[0037] 9. Begin the test and check the sample results. The measured sample concentrations are shown in Table 2.

[0038] Table 2. Serum CHI3L1 levels in the healthy control group, hepatitis group, cirrhosis group, and liver cancer group.

[0039] PC Detection Assay: The Human Phosphatidylcholine (PC) ELISA Kit was purchased from Shanghai Keabob Biotechnology Co., Ltd., catalog number CB11021-Hu. The kit uses a one-step sandwich enzyme-linked immunosorbent assay (ELISA) with double antibodies. The sample, standard, and HRP-labeled detection antibody were added sequentially to the pre-coated microwells containing phosphatidylcholine (PC) antibody, followed by incubation and thorough washing. The substrate TMB was used for color development; TMB is converted to blue under the catalysis of peroxidase, and then to yellow under acidic conditions. The color intensity is positively correlated with the phosphatidylcholine (PC) concentration in the sample. The absorbance (OD value) was measured at 450 nm using a microplate reader, and the sample concentration was calculated.

[0040] The kit includes microplates (12 wells × 8 strips), standards (0.3 mL × 6 tubes, PC standard sample concentrations are: 0 pg / mL, 30 pg / mL, 60 pg / mL, 120 pg / mL, 240 pg / mL, 480 pg / mL), sample diluent (6 mL), detection antibody-HRP (10 mL), 20× wash buffer (25 mL, diluted 1:20 with distilled water), substrate A (6 mL), substrate B (6 mL), stop solution (6 mL), and sealing film (2 sheets).

[0041] The specific operating steps are as follows: 1. First, equilibrate the ELISA plate to room temperature, remove the required strips, seal the remaining strips in a resealable bag and return them to 4°C, and prepare the washing buffer in advance.

[0042] 2. Set up standard wells and sample wells. Add 50 μL of standard at different concentrations to each standard well. Add 10 μL of the sample to be tested to each sample well, followed by 40 μL of sample diluent. Do not add any to the blank wells.

[0043] 3. Except for the blank wells, add 100 μL of detection antibody-HRP to each of the standard and sample wells, seal the reaction wells with sealing film, and incubate at 37°C in a water bath or incubator for 60 min to ensure that the sample and antibody bind fully.

[0044] 4. After incubation, discard the liquid, pat dry on absorbent paper, fill each well with washing solution, let stand for 1 minute, shake off the washing solution, pat dry on absorbent paper, and repeat this washing process 5 times (or a plate washer can be used). This ensures the removal of unbound material and reduces background signal.

[0045] 5. Add 50 μL of substrate A and 50 μL of substrate B to each well and incubate at 37°C in the dark for 15 min.

[0046] 6. After incubation, add 50 μL of stop solution to each well. Within 15 minutes, measure the absorbance of each well at 450 nm using a microplate reader, plot a standard curve, and determine the concentration of the sample to be tested. The measured sample concentrations are shown in Table 3.

[0047] Table 3. Serum PC values ​​in the healthy control group, hepatitis group, cirrhosis group, and liver cancer group.

[0048] Analysis of the data in Tables 1, 2, and 3 showed that, compared with the healthy control group, AFP levels in the hepatitis group were not significantly different, while CHI3L1 and PC levels were significantly elevated, indicating that CHI3L1 and PC are valuable clinical indicators for the auxiliary diagnosis of hepatitis. Compared with the healthy group, AFP, CHI3L1, and PC levels were significantly elevated in both the cirrhosis group and the liver cancer group, suggesting that AFP, CHI3L1, and PC can be used as auxiliary diagnostic indicators for cirrhosis and liver cancer.

[0049] Third, generate ROC curves to clarify the specificity and sensitivity of single-indicator detection and multi-indicator combined detection for liver diseases.

[0050] Sensitivity measures a model's ability to correctly identify positive samples; it's the proportion of samples that the model predicts to be positive out of all actual positive samples. High sensitivity means fewer missed diagnoses, and in medical testing, high sensitivity is often important to avoid missing truly infected patients.

[0051] Specificity measures a model's ability to correctly identify negative samples; it's the proportion of samples that the model predicts to be negative out of all actual negative samples. High specificity means fewer false alarms, and it's crucial in scenarios where strict control of false alarms is necessary.

[0052] Sensitivity and specificity are the core indicators for measuring the accuracy of diagnostic tests. Improving both simultaneously means that the test has been substantially enhanced in its ability to distinguish between diseased and healthy populations. More accurate tests can reduce unnecessary follow-up examinations, more precisely target the population that truly needs treatment, and improve the efficiency of medical resource utilization.

[0053] ROC curves for single-indicator and multi-indicator joint analysis were generated using IBM SPSS Statistics 26 and GraphPad Prism 8 software. The ROC curve is a curve obtained by plotting the true positive rate and false positive rate, and can be used to reflect the relationship between sensitivity and specificity. It is plotted with sensitivity on the ordinate and 1 − specificity on the abscissa, with a series of cutoff values ​​for the experimental and control groups. Sensitivity and specificity are calculated separately for each cutoff value, and the lines connecting these points form the ROC curve. Currently, ROC curves are mostly used to evaluate the discriminative power of clinical risk prediction models that predict the risk of occurrence (diagnostic studies) or development (prognostic studies) of events; that is, the ability to distinguish between patients who have events and those who do not by predicting a higher risk. AUC is used to quantify the ROC curve; it represents the area under the ROC curve and the coordinate axes, usually above the diagonal (the line y=x), so the AUC range is 0.5–1.0. The larger the AUC value, the better the predictive model, i.e., the higher the discriminative power.

[0054] An AUC value between 0.5 and 1.0 is generally considered a good result, indicating that the model has some predictive ability; between 0.7 and 0.8 is generally considered a moderate result, indicating that the model has some predictive ability but there is still room for improvement; between 0.8 and 0.9 is generally considered a good result, indicating that the model has high predictive ability; and between 0.9 and 1.0 is generally considered a very good result, indicating that the model has very high predictive ability.

[0055] The specific operating steps are as follows: Single-index ROC curve: 1. Open GraphPad Prism. In the welcome screen that pops up, select Column in New table & graph on the left. In Data table, select Enter or import data into a new table. In Options, select Enter replicate values, stacked into columns. Click create.

[0056] 2. Enter the required data in columns A and B, representing the healthy control group and the disease group.

[0057] 3. In the Analysis option group in the toolbar, select the Analyze command icon. In the newly popped-up Analyze Data command box, select Column analyses, then select the ROC Curve option. Check the two datasets A: Healthy Controls and B: Disease in the right-hand box, and click OK. In the pop-up ROC curve parameter settings window, keep the default options and click OK.

[0058] 4. The ROC curve can be viewed in Graphs, and the ROC analysis results can be viewed in Results.

[0059] 5. In the results, examine the specificity and sensitivity of each point on the ROC curve, and calculate the Youden index (Youden index = sensitivity + specificity - 1). The index value corresponding to the maximum Youden index is the optimal threshold.

[0060] Joint ROC curve of multiple indicators: When it's necessary to plot an ROC curve combining two or three variables, a binary logistic regression model is used to calculate the predicted probabilities of hepatitis, cirrhosis, and liver cancer in the subjects, using AFP, CHI3L1, and PC factors. We need to perform transformations in SPSS to obtain the combined ROC curve for multiple variables.

[0061] 1. Open SPSS Statistics 26, click Data View, and enter the AFP, CHI3L1, and PC values ​​for the healthy control group and the disease group; then click Variable View and assign values ​​to the data (1 for the healthy control group and 2 for the disease group). Name this column Disease Group (Hepatitis / Cirrhosis / Liver Cancer).

[0062] 2. Click "Analyze → Regression → Binary Logistic Regression" to perform the regression calculation.

[0063] 3. Place AFP, CHI3L1 / AFP, PC / CHI3L1, PC / AFP, CHI3L1, and PC into the covariate box and the disease group into the dependent variable box. Click "Save", check "Probability" in the predicted value section, and click "Continue".

[0064] 4. Click on the data view. PRE-1 is the data from which we plot the ROC curve when combining 2 or 3 variables. Next, run the ROC curve.

[0065] 5. Click "Analyze → Classify → ROC Curve" to draw the ROC curve.

[0066] 6. Place the predicted probability (PRE-1) in the test variable box, the disease group in the state variable box, set the state variable value to 2, check all display areas, and click "OK" to generate the ROC curve and the area under the curve.

[0067] 7. You can also use GraphPad Prism to create ROC curves, which is similar to drawing single-index ROC curves. The difference is that the inputs in Group A and B columns are PRE-1 data generated by binary logistic regression, representing the healthy control group and the disease group. The subsequent steps are the same as above.

[0068] like Figures 3-9 The following are the ROC curves for serum AFP, CHI3L1, and PC in the hepatitis group, respectively; the combined ROC curves for serum AFP and PC in the hepatitis group; the combined ROC curves for serum CHI3L1 and PC in the hepatitis group; the combined ROC curves for serum AFP and CHI3L1 in the hepatitis group; and the combined ROC curves for serum AFP, CHI3L1, and PC in the hepatitis group. The ROC analysis results for different detection indicators in the hepatitis group are shown in Table 4.

[0069] Table 4. ROC analysis results of different detection indicators in the hepatitis group.

[0070] The ROC results showed that serum levels of the two biomarkers, CHI3L1 and PC, were significantly higher in the hepatitis group than in the healthy group (P<0.0001). The AUC values ​​for AFP in diagnosing hepatitis were 0.5091, for CHI3L1 0.8866, and for PC 0.9683. The threshold for CHI3L1 in the hepatitis group was 63.05 ng / mL, and for PC it was 1066 pg / mL. Therefore, CHI3L1 and PC can serve as biomarkers for hepatitis detection, while AFP cannot.

[0071] The AUC values ​​for AFP combined with PC in diagnosing hepatitis were 0.9660, AFP combined with CHI3L1 in diagnosing hepatitis were 0.8821, CHI3L1 combined with PC in diagnosing hepatitis were 0.9955, and AFP combined with CHI3L1 and PC in diagnosing hepatitis were 1, with P values ​​less than 0.0001 for all values. This indicates that the combined use of two or three indicators can serve as biomarkers for hepatitis detection, and the combined use of two or three indicators is superior to single-indicator diagnosis. PC, in particular, significantly improves the sensitivity and specificity in diagnosing hepatitis.

[0072] like Figures 10-16 The following figures show the ROC curves for serum AFP, CHI3L1, and PC in the cirrhosis group, respectively; the combined ROC curves for serum AFP and PC in the cirrhosis group; the combined ROC curves for serum CHI3L1 and PC in the cirrhosis group; the combined ROC curves for serum AFP and CHI3L1 in the cirrhosis group; and the combined ROC curves for serum AFP, CHI3L1, and PC in the cirrhosis group. The ROC analysis results for different detection indicators in the cirrhosis group are shown in Table 5.

[0073] Table 5. ROC analysis results of different detection indicators in the liver cirrhosis group.

[0074] The ROC results showed that serum levels of three biomarkers, AFP, CHI3L1, and PC, were significantly higher in the cirrhosis group than in the healthy group (P<0.0001, P<0.0004 for AFP). The AUC values ​​for diagnosing cirrhosis were 0.8163 for AFP, 0.9410 for CHI3L1, and 0.9229 for PC. The threshold values ​​for AFP, CHI3L1, and PC in the cirrhosis group were 3.585 ng / mL, 64.35 ng / mL, and 872.8 pg / mL, respectively. Therefore, AFP, CHI3L1, and PC can serve as biomarkers for detecting cirrhosis.

[0075] The AUC values ​​for AFP combined with PC in diagnosing liver cirrhosis were 0.9082, AFP combined with CHI3L1 in diagnosing liver cirrhosis were 1, CHI3L1 combined with PC in diagnosing liver cirrhosis were 1, and AFP combined with CHI3L1 and PC in diagnosing liver cirrhosis were all 1, with P values ​​less than 0.0001. This indicates that the combined diagnosis of two or three indicators can serve as biomarkers for detecting liver cirrhosis, and the combined diagnosis of two or three indicators is superior to single-indicator diagnosis. Specifically, CHI3L1 significantly improves the sensitivity and specificity in the detection of liver cirrhosis, while PC can improve the sensitivity and specificity to some extent in the detection of liver cirrhosis.

[0076] like Figures 17-23 The following figures show the ROC curves for serum AFP, CHI3L1, and PC in the liver cancer group, respectively; the combined ROC curves for serum AFP and PC in the liver cancer group; the combined ROC curves for serum CHI3L1 and PC in the liver cancer group; the combined ROC curves for serum AFP and CHI3L1 in the liver cancer group; and the combined ROC curves for serum AFP, CHI3L1, and PC in the liver cancer group. The ROC analysis results for different detection indicators in the liver cancer group are shown in Table 6.

[0077] Table 6. ROC analysis results of different detection indicators in the liver cancer group.

[0078] The ROC results showed that serum levels of three biomarkers, AFP, CHI3L1, and PC, were significantly higher in the hepatocellular carcinoma (HCC) group than in the healthy group (P<0.0001). The AUC value for AFP in diagnosing HCC was 1; the AUC value for CHI3L1 in diagnosing HCC was 0.9977; and the AUC value for PC in diagnosing HCC was 0.9973. The threshold values ​​for AFP, CHI3L1, and PC in the HCC group were 10693 ng / mL, 79.65 ng / mL, and 1878 pg / mL, respectively. These results indicate that AFP, CHI3L1, and PC can serve as biomarkers for detecting HCC.

[0079] Therefore, CHI3L1 and PC can be used as biomarkers for detecting hepatitis, while AFP, CHI3L1, and PC can be used as biomarkers for detecting cirrhosis and liver cancer. The AUC values ​​for AFP combined with PC in diagnosing liver cancer were 1, AFP combined with CHI3L1 in diagnosing liver cancer were 1, CHI3L1 combined with PC in diagnosing liver cancer were 1, and AFP combined with CHI3L1 and PC in diagnosing liver cancer were also 1, with P values ​​less than 0.0001 for all values. This demonstrates that the combined diagnosis of two or three indicators can serve as biomarkers for detecting liver cirrhosis, and the combined diagnosis of two or three indicators is superior to single-indicator diagnosis. PC can improve the sensitivity and specificity of cirrhosis detection to a certain extent.

[0080] PC can be used as a biomarker in serological tests to help detect and diagnose liver diseases, thus enabling appropriate treatment measures to be taken as early as possible.

[0081] PC has high sensitivity and specificity for patients with liver diseases. In the experiment, the sensitivity and specificity for hepatitis patients reached 95.24% and 95.24%, respectively; for cirrhosis patients, the sensitivity and specificity reached 80.95% and 90.48%, respectively; and for liver cancer patients, the sensitivity and specificity reached 95.24% and 95.24%, respectively.

[0082] PC can be used in combination with other molecular markers such as AFP and CHI3L1 for diagnosis, which can improve the specificity and sensitivity of disease diagnosis and further improve the accuracy of liver disease diagnosis.

[0083] PC, as a newly discovered diagnostic indicator for liver diseases, has the highest predictive ability in hepatitis, and a relatively high predictive ability in cirrhosis and liver cancer. It can also improve the predictive ability of other indicators for liver diseases, and has very high clinical value.

[0084] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A biomarker for assisting in the diagnosis of liver diseases, characterized in that, It includes serum phosphatidylcholine, as well as any one or two of alpha-fetoprotein and chitosanase 3-like protein 1.

2. The biomarker for assisting in the diagnosis of liver diseases according to claim 1, characterized in that, The liver disease mentioned is one or more of hepatitis, cirrhosis, and liver cancer.

3. The use of the biomarker for assisting in the diagnosis of liver diseases as a target for the preparation, detection, and screening of drugs for liver diseases, as described in claim 2.

4. A reagent kit for detecting liver diseases, characterized in that, The kit includes reagents for detecting the biomarkers for the auxiliary diagnosis of liver diseases as described in claim 1.

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