Marker group for early diagnosis and early warning of intrapulmonary vasodilatation and liver-lung syndrome and application of marker group

Abstract

The invention discloses a marker group for early diagnosis and early warning of intrapulmonary vasodilatation and hepatic and pulmonary syndromes and application of the marker group. The biomarkers are S1P, angiopoietin-2 and PDGF-BB (Platelet Derived Growth Factor-Bulk Broadcast) The application is any one or more of the following applications: application in preparation of products for early diagnosis of intrapulmonary vasodilatation and liver-lung syndrome; the invention also relates to an application in preparing a product for early warning intrapulmonary vasodilatation and liver-lung syndrome. Application in products for screening intrapulmonary vasodilatation and liver-lung syndrome; the invention also relates to application in products for detecting intrapulmonary vasodilatation and liver-lung syndrome. The kit has high detection sensitivity and specificity, can accurately detect intrapulmonary vasodilatation and liver-lung syndrome, can carry out early warning and diagnosis on early HPS, has good detection stability, and has a good clinical application prospect.

Description

Technical Field

[0001] The present invention relates to a new use of a biomarker group, specifically an application of a substance for detecting the expression level of a biomarker in a sample to be tested in the preparation of an early diagnosis, auxiliary diagnosis or early warning product for pulmonary vasodilation and hepatopulmonary syndrome and a detection kit, belonging to the field of biotechnology. Background Art

[0002] Hepatopulmonary syndrome (HPS) is a clinical condition that occurs in the context of chronic liver disease and / or portal hypertension, characterized by hypoxemia caused by intrapulmonary vasodilation (IPVD) and abnormal arterial oxygenation, as well as related pathophysiological changes and clinical manifestations. The main causes of HPS include advanced liver disease, portal hypertension, or congenital portosystemic shunts. The prevalence of HPS ranges from 4% to 47% in patients with cirrhosis, while IPVD can be detected in 13% to 80% of liver transplant candidates. A prospective study of 111 patients with cirrhosis showed that the median survival time of patients without HPS was 40.8 months, while the median survival time of patients with HPS was only 10.6 months, indicating that HPS is an independent risk factor for poor prognosis in patients with cirrhosis.

[0003] Although the correlation between HPS and chronic liver and lung diseases has been widely recognized, its specific pathogenesis remains unclear and there are certain challenges in diagnosis. At present, there is a lack of specific drugs for HPS in clinical practice, and liver transplantation is still the only effective treatment for HPS. Therefore, early diagnosis of HPS is crucial so that patients can be included in the liver transplant candidate list in a timely manner. HPS patients may only show latent dyspnea or even no symptoms in the early stage, which leads to a high rate of early misdiagnosis. About 25% of patients have worsening symptoms of dyspnea and hypoxemia in an upright position, which may be because IPVD mainly occurs at the base of the lungs, and blood flow in this area increases when the patient is in an upright position. Therefore, early detection and early warning of IPVD are crucial for the diagnosis and early warning of HPS. At present, patents have disclosed that PDGF-C can be used as a diagnostic marker for HPS, but its detection effect is not ideal, making the early detection of HPS and IPVD still challenging. The current gold standard for diagnosing HPS is a combination of contrast-enhanced transthoracic echocardiography (CE-TTE) and alveolar-arterial oxygen gradient [P(Aa)O2]. However, only 0.45% of patients with liver disease are diagnosed with HPS, and the accuracy of diagnosis is only 22.5%, which further highlights the need for more sensitive and specific marker combinations and their construction models in the early diagnosis of HPS. Summary of the invention

[0004] The primary technical problem to be solved by the present invention is to provide a biomarker group for early diagnosis, auxiliary diagnosis and early warning of intrapulmonary hemorrhage and hepatopulmonary syndrome.

[0005] Another technical problem to be solved by the present invention is to provide a new use of the above biomarker panel.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions: According to a first aspect of an embodiment of the present invention, there is provided an application of a substance for detecting a biomarker in a sample to be tested, wherein the biomarker is one or more of S1P, angiopoietin-2 and PDGF-BB; the application is any one or more of the following: A1. Application in the preparation of products for early diagnosis of pulmonary vasodilation and / or hepatopulmonary syndrome; A2. Application in the preparation of products for early warning of pulmonary vasodilation and / or hepatopulmonary syndrome; A3. Application in the preparation of products for screening pulmonary vasodilation and / or hepatopulmonary syndrome; A4. Application in the preparation of products for detecting pulmonary vasodilation and / or hepatopulmonary syndrome; A5. Application in the preparation of products for evaluating pulmonary vasodilation and / or hepatopulmonary syndrome.

[0007] According to a second aspect of an embodiment of the present invention, there is provided an application of a substance for detecting the expression level of a biomarker in a test sample in the preparation of a model for early warning and diagnosis of pulmonary vasodilation and hepatopulmonary syndrome, wherein the biomarker is one or more of S1P, angiopoietin-2 and PDGF-BB.

[0008] Preferably, the biomarkers are S1P, angiopoietin-2 and PDGF-BB.

[0009] Preferably, the substance for detecting S1P is a human S1P ELISA kit; the substance for detecting angiopoietin-2 is a human angiopoietin-2 ELISA kit; and the substance for detecting PDGF-BB is a PDGF-BB immunoassay kit.

[0010] Compared with the prior art, the present invention has the following technical effects: (1) Provide a group of biomarkers that are closely related to the occurrence and development of intrapulmonary vasodilation (IPVD) and hepatopulmonary syndrome (HPS). IPVD is considered to be the early stage of HPS, and this marker group has been experimentally verified to be able to effectively perform early diagnosis, early warning, screening, detection and evaluation of IPVD. In addition, this marker group can also perform early diagnosis, early warning, screening, detection and evaluation of the process of IPVD developing into HPS, and these applications have shown high sensitivity, specificity and stability.

[0011] (2) Prediction models for IPVD and HPS were constructed using these markers. These models used LASSO regression analysis to improve the accuracy of prediction. In the prediction model for IPVD, the area under the receiver operating characteristic curve (AUC) reached 0.792, with a 95% confidence interval of 0.737 to 0.847. For the prediction model for HPS, the AUC value was higher, reaching 0.891, with a 95% confidence interval of 0.848 to 0.934, indicating that the model has a high prediction accuracy.

[0012] (3) Provide an HPS assessment model based on these markers. This model can assess the patient's HPS status based on the indicators and their reference ranges, and has an auxiliary role in the diagnosis and identification of HPS. By applying this model, a detection kit suitable for HPS diagnosis and early warning can be formed, providing a practical tool for clinical use. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1A The Kruskal-Wallis rank sum test was used to compare the serum S1P concentrations among multiple groups in patients without IPVD, cirrhotic patients with IPVD without HPS, and cirrhotic patients with HPS; Figure 1B The Kruskal-Wallis rank sum test was used to compare the concentrations of serum Ang-2 among patients without IPVD, cirrhotic patients with IPVD without HPS, and cirrhotic patients with HPS; Figure 1C The Kruskal-Wallis rank sum test was used to compare the serum PDGF-BB concentrations among multiple groups in patients without IPVD, cirrhotic patients with IPVD without HPS, and cirrhotic patients with HPS; Figure 2A AUC curves for predicting IPVD for different parameters; Figure 2B AUC curves for predicting HPS for different parameters; Figure 3A To describe the correlation between clinical parameters and biomarkers, the heat map shows the Spearman correlation coefficient r of patient samples; Figure 3BHeatmap depicting the correlation between clinical parameters and biomarkers, with the corresponding P value for each correlation coefficient r; Figure 4A A nomogram for the IPVD prediction model in patients with cirrhosis that integrates 4 clinical parameters and 3 biomarkers; Figure 4B This is the ROC curve prediction diagram for patients with cirrhosis combined with IPVD; Figure 4C is the calibration curve of the nomogram used to predict IPVD; Figure 4D It is a decision curve analysis diagram; Figure 5A The nomogram for the HPS prediction model in patients with cirrhosis; Figure 5B ROC curve prediction for HPS in patients with cirrhosis; Figure 5C is a calibration curve graph of the nomogram used to predict HPS; Figure 5D It is a decision curve analysis diagram; Figure 6 is the AUC graph of each marker group in IPVD; Figure 7 It is the AUC graph of each marker group in HPS. DETAILED DESCRIPTION

[0014] In order to understand the present invention more deeply, a more detailed description will be provided below in conjunction with the accompanying drawings. The accompanying drawings show the best embodiments of the present invention, but it should be noted that the present invention is not limited to these specific embodiments. The purpose of providing these embodiments is to help the public understand the content of the present invention more comprehensively, rather than to limit the scope of the present invention.

[0015] In the specification of the present invention, all technical and scientific terms, unless otherwise defined, should be interpreted according to the meanings commonly understood by those skilled in the art. The use of these terms is only to describe specific embodiments and is not intended to limit the scope of protection of the present invention.

[0016] The description of the technical features of the present invention includes both open and closed forms. The open description includes the listed features, and also includes any other open technical solutions containing these features. In the case of numerical intervals, unless otherwise specified, these numerical intervals are regarded as continuous, including the minimum and maximum values ​​of the interval, and all values ​​between these two extreme values. When referring to integer ranges, all integers between the minimum and maximum values ​​are also included. In addition, when multiple ranges are provided to describe features or characteristics, these ranges can be combined. That is, unless otherwise explicitly stated, all ranges disclosed in the present invention should be understood to include all sub-ranges therein.

[0017] Some numerical ranges disclosed in the present invention can be freely combined with any lower limit and upper limit to form a new range, and each separately disclosed point or single value can also be used as the lower limit or upper limit of a new range. The temperature parameters in the present invention, unless otherwise specified, can be constant temperature treatment or can be changed within a certain temperature range, and temperature fluctuations within the control accuracy range of the instrument are allowed.

[0018] In the description of the present invention, "multiple" refers to at least two, such as two or three, etc., unless otherwise clearly defined. All embodiments of the present invention and optional embodiments thereof, as well as all technical features and optional technical features, can be combined with each other to form a new technical solution. The raw materials and equipment used in the present invention are well known to those skilled in the art and can be prepared by themselves according to the prior art or purchased through commercial channels.

[0019] The present invention constructs a prediction model for hepatopulmonary syndrome (HPS) in patients with cirrhosis, which is based on clinical parameters and biomarkers in the blood. Compared with the current gold standard for diagnosing HPS, the prediction model provided by the present invention shows superior performance in terms of sensitivity and specificity. This improvement makes the prediction model have important application value in the screening and continuous evaluation of HPS in the clinic.

[0020] In the process of establishing this prediction model, a group of novel biomarkers that may be used to distinguish whether patients with cirrhosis have HPS were found through screening and evaluation. This group of markers includes S1P, Angiopoietin-2 (Ang-2), and PDGF-BB. The combination of these biomarkers shows potential clinical application prospects in identifying the presence or absence of HPS in patients with cirrhosis.

[0021] Example 1 Screening for markers most closely related to the occurrence and development of IPVD and HPS and early joint detection of HPS Methods for establishing diagnostic models (1) A total of 465 patients with cirrhosis who were hospitalized in Capital Medical University You'an Hospital from April 2023 to May 2024 were included in the screening. The patients' demographic data, clinical information, and laboratory indicators were collected through the electronic medical record system. Blood samples of eligible patients were retained for biomarker testing.

[0022] (2) The following data were collected from all patients: demographic information (sex, age, height, weight, etc.), etiology of cirrhosis (alcoholic hepatitis, hepatitis B virus (HBV), hepatitis C virus (HCV), etc.), comorbidities (history of hypertension and diabetes, etc.), disease severity assessment (Child-Pugh grade and Model for End-Stage Liver Disease (MELD) score), arterial blood gas results, laboratory indicators (complete blood count, liver function tests, renal function tests, coagulation function, infection markers, etc.), and cardiac ultrasound examination results.

[0023] (3) A total of 10 ml of whole blood was drawn from patients who agreed to participate in the study and placed in EDTA anticoagulant tubes. Plasma was separated by centrifugation at 2000 g for 10 minutes and stored frozen at -80°C for the detection of plasma cytokine levels.

[0024] Three biomarkers were detected by enzyme-linked immunosorbent assay: human S1P ELISA kit (Catalog No: EKF 58355), human angiopoietin-2 ELISA kit (Catalog No: KHC 1641), and human VWF ELISA kit (Catalog No: KHC 1641). Six other cytokines were detected using a customized ProcartaPlex 6-plex method, including intercellular adhesion molecule-1 (ICAM-1), platelet-derived growth factor BB (PDGF-BB), tumor necrosis factor α (TNF-α), soluble E-selectin, vascular cell adhesion molecule-1 (VCAM-1), and vascular endothelial growth factor A (VEGF-A), and the data were read on a Luminex 200 instrument (Luminex Corporation).

[0025] (4) In the process of constructing the model, the present invention uses the LASSO regression method to screen key modeling factors. LASSO regression is a statistical technique that can reduce model overfitting. It reduces the complexity of the model by introducing a penalty term. In order to determine the optimal model parameter λ, the present invention uses 10-fold cross validation, which is a widely recognized model validation method that can improve the accuracy and reliability of model parameter selection.

[0026] In order to evaluate the classification and diagnostic capabilities of the model, the present invention uses the receiver operating characteristic (ROC) curve. The ROC curve is a graphical tool used to show the performance of the model under different threshold settings. The performance of the model is quantified by the area under the curve (AUC), and the higher the AUC value, the stronger the diagnostic ability of the model. In addition, the optimal cutoff value is determined by the Youden index, which is a statistic that comprehensively considers sensitivity and specificity to find the best balance point.

[0027] The present invention also lists in detail other performance indicators of the model, including critical value, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). These indicators provide a comprehensive evaluation of the diagnostic performance of the model.

[0028] In order to further analyze the relationship between different clinical parameters and biomarkers, the present invention uses a heat map to display the results of the correlation analysis. In the heat map, the larger the absolute value of the correlation coefficient r, the stronger the correlation between the two variables; a positive r value indicates a positive correlation, while a negative r value indicates a negative correlation. In this way, it is possible to intuitively identify which parameters are strongly associated with the occurrence of HPS.

[0029] Finally, the present invention constructed a nomogram by integrating the seven identified predictors, which is a graphical tool for displaying risk prediction for individual patients. This nomogram can help doctors and researchers intuitively assess the risk of HPS based on the patient's clinical parameters and biomarker levels.

[0030] Example 2 Screening and analysis of markers most closely related to the occurrence and development of IPVD and HPS After screening, a total of 320 patients were included in the analysis, 101 patients met the diagnosis of IPVD, of which 54 were diagnosed with HPS. Statistical analysis was performed on the biomarkers of three groups of cirrhosis patients after hospitalization: no IPVD, IPVD without HPS, and HPS. We found that five biomarkers in the three groups were statistically different (P < 0.05), namely S1P, Ang-2, ICAM-1, PDGF-BB and VEGF-A, as shown in Table 1, suggesting that the above five markers may be used as early warning and detection markers for IPVD and HPS in patients with cirrhosis.

[0031] Explanation of the above groupings: No IPVD refers to patients with cirrhosis who have not developed IPVD; IPVD without HPS refers to patients with cirrhosis combined with IPVD but without HPS; HPS refers to patients with cirrhosis combined with HPS; HPS is diagnosed on the basis of IPVD, and IPVD must occur first before HPS can be diagnosed.

[0032] Table 1 Biomarkers in hospitalized patients with cirrhosis with or without IPVD and HPS

[0033] The above patients were divided into the non-IPVD group and the IPVD group. The IPVD group included patients with simple IPVD and patients who were finally diagnosed with HPS. The predictive markers between the two groups were compared. The results showed that S1P, Ang-2, ICAM-1, and PDGF-BB still had statistical differences (P < 0.05), and VEGF-A had no statistical difference, as shown in Table 2. The above patients were divided into the non-HPS group and the HPS group. The non-HPS group included the non-IPVD group and the simple IPVD group. The predictive markers between the two groups were compared. The results showed that the five markers S1P, Ang-2, ICAM-1, PDGF-BB and VEGF-A had statistical differences (P < 0.05) as shown in Table 3.

[0034] Table 2 Biomarkers in hospitalized patients with cirrhosis without and with IPVD

[0035] Table 3 Biomarkers in hospitalized patients with cirrhosis without and with HPS

[0036] We used univariate logistic regression analysis to screen variables that predict intrapulmonary vasodilation (IPVD) and hepatopulmonary syndrome (HPS). First, we compared the variables between the IPVD-free group and the IPVD-with group, and found that among the variables with significant statistical differences between the two groups, the three biomarkers S1P, Ang-2, and PDGF-BB also showed significant statistical differences in the univariate logistic regression analysis (P < 0.05). The specific results are shown in Table 4. Similarly, when we compared the HPS-free group and the HPS-with group, the three biomarkers S1P, Ang-2, and PDGF-BB also showed statistically significant differences (P < 0.05). The relevant data are shown in Table 5. These findings suggest that S1P, Ang-2, and PDGF-BB may be potential biomarkers for predicting IPVD and HPS.

[0037] Table 4 Univariate logistic regression analysis of biomarkers predicting IPVD in patients with cirrhosis

[0038] Table 5 Univariate Logistic regression analysis of biomarkers predicting HPS in patients with cirrhosis

[0039] exist Figures 1A to 1CIn this study, we compared the levels of three biomarkers (S1P, Ang-2, and PDGF-BB) among patients with cirrhosis without IPVD (labeled as "Without IPVD"), with IPVD but without HPS (labeled as "Without HPS"), and with HPS (labeled as "With HPS"). The results showed that the S1P level in the group without IPVD was higher than that in the group with HPS, but there was no statistically significant difference in S1P level between the group without IPVD and the group with IPVD without HPS, and between the group with IPVD without HPS and the group with HPS (see Figure 1A ). The levels of Ang-2 showed significant statistical differences among the three groups, with the lowest level in the group without IPVD, higher levels in the group with IPVD but without HPS, and the highest level in the group with HPS (see Figure 1B ). The level of PDGF-BB was the highest in the group without IPVD and was statistically significantly different from the group with IPVD without HPS and the group with HPS, whereas there was no statistically significant difference in the level of PDGF-BB between the group with IPVD without HPS and the group with HPS (see Figure 1C ). These research results indicate that the combined detection of S1P, Ang-2 and PDGF-BB can be used as a potential means of detecting and early warning of IPVD and early HPS.

[0040] We used LASSO logistic regression to screen out key variables that predict the occurrence of intrapulmonary vasodilation (IPVD) and hepatopulmonary syndrome (HPS) in patients with cirrhosis, including clinical parameters. Figure 2A The area under the receiver operating characteristic (ROC) curve (AUC) for predicting IPVD was shown, among which the AUC for age was 0.613 (95% confidence interval CI: 0.546-0.679), the AUC for MELD score was 0.691 (95% CI: 0.629-0.753), the AUC for alveolar-arterial oxygen gradient [P(Aa)O2] was 0.592 (95% CI: 0.496-0.633), the AUC for S1P was 0.649 (95% CI: 0.584-0.714), the AUC for Ang-2 was 0.699 (95% CI: 0.638-0.760), and the AUC for PDGF-BB was 0.647 (95% CI: 0.583-0.711). Figure 2BThe area under the ROC curve for predicting HPS was shown, with the AUC for age being 0.618 (95% CI: 0.542-0.693), the AUC for [P(Aa)O2] being 0.837 (95% CI: 0.790-0.885), the AUC for hemoglobin (HGB) being 0.674, the AUC for total bilirubin (TBIL) being 0.749, the AUC for albumin (ALB) being 0.734 (95% CI: 0.667-0.801), the AUC for S1P being 0.635 (95% CI: 0.556-0.713), the AUC for Ang-2 being 0.726 (95% CI: 0.652-0.800), and the AUC for PDGF-BB being 0.599 (95% CI: 0.519-0.679). These AUC values ​​reflect the effectiveness of each predictor in predicting IPVD and HPS, and the predictive value information of the combined model is listed in detail in Table 6.

[0041] Table 6 Predictive value of different parameters and combined models for predicting IPVD and HPS in patients with cirrhosis

[0042] In this paper, we performed pairwise correlation comparisons on all variables and identified variables with statistically significant differences in univariate logistic regression analysis. The relevant results are shown in Figure 3A and Figure 3B middle. Figure 3A The Spearman rank correlation coefficient (r) is used to represent the correlation between the parameters, where the color mapping table ranges from 1 to -1, with blue representing the highest correlation and red representing the lowest correlation. Figure 3B The corresponding P value is displayed, and the color mapping table is from 0 to 1. Blue indicates the maximum P value, white indicates the minimum P value, and white cells without numerical values ​​mean that the P value is less than 0.001, indicating that the correlation is extremely significant. For example, the correlation coefficients (r values) between age and the international normalized ratio (INR), prothrombin time (PT), and prothrombin time activity (PTA) are -0.229, -0.231, and 0.231, respectively, and all P values ​​are less than 0.001, showing a strong statistical correlation. In addition, the correlation between the six numerical parameters in the study was not statistically significant, indicating that there is no collinearity between them, which further shows that the combination of these parameters is more effective in predicting the presence of HPS in patients with cirrhosis, such as the correlation coefficient P values ​​between age and other parameters are all greater than 0.05.

[0043] Figure 4A and Figure 4BA prediction model for IPVD integrating clinical parameters and biomarkers was presented. The model was developed based on LASSO regression analysis and included seven predictors: age, alveolar-arterial oxygen gradient (P(Aa)O2), hemoglobin (HGB), total bilirubin (TBIL), albumin (ALB), sphingolipid-1-phosphate (S1P), angiopoietin-2 (Ang-2), and platelet-derived growth factor BB (PDGF-BB). Figure 4A The nomogram in shows that the model combines four clinical parameters and three biomarkers, while Figure 4B The area under the ROC curve (AUC) was 0.792, and the 95% confidence interval [CI] was 0.737-0.847, indicating that the model had good predictive ability. Figure 4C The calibration curve of the nomogram for predicting IPVD was provided, with a P value of 0.153, indicating that the model had a good fit. Figure 4D The results of this study indicate that the model can effectively assist clinicians in identifying cirrhotic patients with IPVD.

[0044] FIG. 5A to FIG. 5D A prediction model for hepatopulmonary syndrome (HPS) combining clinical parameters and biomarkers was presented. Specifically, Figure 5A A nomogram for HPS prediction in patients with cirrhosis is provided. Figure 5B The area under the receiver operating characteristic (ROC) curve (AUC) of the model was 0.891, and its 95% confidence interval (CI) was between 0.848 and 0.934, which indicated that the model had high predictive accuracy. Figure 5C The calibration curve in shows that the model fits well, with a P value of 0.548, which further confirms the reliability of the model. Figure 5D The decision curve analysis in the study showed that the model can effectively assist clinicians in identifying HPS patients, thus playing an important role in clinical practice.

[0045] Example 3 Verification of sensitivity and specificity of each marker and screening and analysis of marker groups In order to improve the accuracy of early warning of intrapulmonary vasodilation (IPVD) and hepatopulmonary syndrome (HPS), the inventors found that compared with single marker detection, the use of marker combinations for detection can achieve higher sensitivity, specificity and accuracy. Therefore, the inventors conducted an in-depth screening of marker combinations in order to find a better marker combination for the detection of IPVD and HPS. After screening, the inventors identified a group of markers that showed higher diagnostic performance than single markers in early warning of IPVD and HPS. The specific screening results are described as follows.

[0046] The sensitivity and specificity of each marker in the IPVD were verified using the present invention. The AUC data are shown in Table 7, and the ROC information is shown in Table 8.

[0047] Table 7

[0048] Table 8

[0049] The sensitivity and specificity of each marker in the HPS were verified using the present invention. The AUC data are shown in Table 9, and the ROC information is shown in Table 10.

[0050] Table 9

[0051] Table 10

[0052] From the above results, it can be seen that S1P, Ang-2, PDGF-BB, ICM-1 and VEGF-A are more excellent in various parameters in IPVD and HPS, and may be members of the marker group. Therefore, the above markers were selected for combination for the following efficacy verification.

[0053] Further validation of the superior marker panel: (1) The efficacy of each marker combination for detection in IPVD was demonstrated. The AUC data of each marker group are shown in Table 11. Figure 6 The ROC information is shown in Table 12, and the marker group description is as follows.

[0054] Marker group 1 (corresponding to Model 1 in the figure): S1P, Ang-2, PDGF-BB; Marker group 2 (corresponding to Model 2 in the figure): S1P, Ang-2, ICAM-1; Marker group 3 (corresponding to Model 3 in the figure): Ang-2, PDGF-BB, VEGF-A; Marker group 4 (corresponding to Model 4 in the figure): Ang-2, ICAM-1, VEGF-A.

[0055] Table 11

[0056] Table 12

[0057] (2) The efficacy of each marker combination in constructing a predictive model was demonstrated in HPS. The AUC data of each marker group are shown in Table 13 and Figure 7 The ROC information is shown in Table 14.

[0058] Marker group 1 (corresponding to Model 1 in the figure): S1P, Ang-2, PDGF-BB; Marker group 2 (corresponding to Model 2 in the figure): S1P, Ang-2, ICAM-1; Marker group 3 (corresponding to Model 3 in the figure): Ang-2, PDGF-BB, VEGF-A; Marker group 4 (corresponding to Model 4 in the figure): Ang-2, ICAM-1, VEGF-A.

[0059] Table 13

[0060] Table 14

[0061] From the above experiments, it can be seen that the specific marker combination of S1P, Ang-2 and PDGF-BB showed obvious superiority in the detection of IPVD and HPS, and its detection effect was not only better than that of a single marker, but also better than other marker combinations. Therefore, this marker combination can be considered as a potential early HPS detection, warning and diagnosis marker due to its high effect in IPVD detection.

Claims

1. The use of a substance for detecting a biomarker in a sample to be tested, characterized in that The biomarkers are S1P, angiopoietin-2 and PDGF-BB; the applications are any one or more of the following: A1. Application in the preparation of products for early diagnosis of pulmonary vasodilation and / or hepatopulmonary syndrome; A2. Application in the preparation of products for early warning of pulmonary vasodilation and / or hepatopulmonary syndrome; A3. Application in the preparation of products for screening pulmonary vasodilation and / or hepatopulmonary syndrome; A4. Application in the preparation of products for detecting pulmonary vasodilation and / or hepatopulmonary syndrome; A5. Application in the preparation of products for evaluating pulmonary vasodilation and / or hepatopulmonary syndrome.

2. The use according to claim 1, characterized in that: The substance for detecting S1P is a human S1P ELISA kit; the substance for detecting angiopoietin-2 is a human angiopoietin-2 ELISA kit; and the substance for detecting PDGF-BB is a PDGF-BB immunoassay kit.

3. Use of a substance for detecting the expression level of a biomarker in a sample to be tested in the preparation of a model for early warning and diagnosis of pulmonary vasodilation and hepatopulmonary syndrome, characterized in that: The biomarkers are S1P, Angiopoietin-2 and PDGF-BB.

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