Marker group for early diagnosis and early warning of pulmonary vasodilation and hepatopulmonary syndrome and use thereof

Abstract

The present invention discloses a panel of markers for the early diagnosis and early warning of pulmonary vasculature and hepatopulmonary syndrome, and their uses. The biomarkers are S1P, angiopoietin-2, and PDGF-BB; the applications include any one or more of the following: use in the preparation of a product for the early diagnosis of pulmonary vasculature and hepatopulmonary syndrome; use in the preparation of a product for early warning of pulmonary vasculature and hepatopulmonary syndrome; use in a product for screening pulmonary vasculature and hepatopulmonary syndrome; and use in a product for detecting pulmonary vasculature and hepatopulmonary syndrome. The present invention has high detection sensitivity and specificity, can accurately detect pulmonary vasculature and hepatopulmonary syndrome, provide early warning and diagnosis of HPS, have good detection stability, and have excellent clinical application prospects.

Description

Technical Field

[0001] The present invention relates to a new use of a biomarker panel, specifically an application of a substance for detecting the expression level of a biomarker in a sample to be tested in the preparation of a product for early diagnosis, auxiliary diagnosis or early warning of 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 associated 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 association between HPS and chronic liver and lung diseases is widely recognized, its specific pathogenesis remains unclear, presenting diagnostic challenges. Currently, there is a lack of effective treatments for HPS, and liver transplantation remains the only effective treatment. Therefore, early diagnosis of HPS is crucial to ensure timely inclusion of patients in liver transplant candidate lists. HPS patients may present with only subtle dyspnea or even no symptoms at all in the early stages, leading to a high rate of misdiagnosis. Approximately 25% of patients experience worsening dyspnea and hypoxemia in the upright position. This is likely because IPVD primarily occurs in the bases of the lungs, where blood flow increases when the patient is upright. Therefore, early detection and early warning of IPVD are crucial for the diagnosis and early warning of HPS. Currently, patents have disclosed PDGF-C as a diagnostic marker for HPS, but its detection results are suboptimal, making the early detection of HPS and IPVD 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, HPS is diagnosed in only 0.45% of patients with liver disease, and the diagnostic accuracy is only 22.5%. This further highlights the need for more sensitive and specific marker combinations and models for 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-mentioned biomarker panel.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] 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; and the application is any one or more of the following:

[0008] A1. Application in the preparation of products for early diagnosis of pulmonary vasodilation and / or hepatopulmonary syndrome;

[0009] A2. Use in the preparation of products for early warning of pulmonary vasodilation and / or hepatopulmonary syndrome;

[0010] A3. Application in the preparation of products for screening pulmonary vasodilation and / or hepatopulmonary syndrome;

[0011] A4. Application in the preparation of products for detecting pulmonary vasodilation and / or hepatopulmonary syndrome;

[0012] A5. Application in the preparation of products for evaluating pulmonary vasodilation and / or hepatopulmonary syndrome.

[0013] 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.

[0014] Preferably, the biomarkers are S1P, angiopoietin-2 and PDGF-BB.

[0015] 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.

[0016] Compared with the prior art, the present invention has the following technical effects:

[0017] (1) Provide a set of biomarkers that are closely related to the occurrence and development of intrapulmonary vasodilation (IPVD) and hepatopulmonary syndrome (HPS). IPVD is considered to be an 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.

[0018] (2) Predictive 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 even higher, reaching 0.891, with a 95% confidence interval of 0.848 to 0.934, indicating that the model had high predictive accuracy.

[0019] (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 differentiation of HPS. By applying this model, a detection kit suitable for HPS diagnosis and early warning can be developed, providing a practical tool for clinical use. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1A The Kruskal-Wallis rank sum test was used to compare serum S1P concentrations among multiple groups in patients without IPVD, cirrhosis with IPVD without HPS, and cirrhosis with HPS;

[0021] Figure 1B The Kruskal-Wallis rank sum test was used to compare the serum Ang-2 concentrations among multiple groups in patients without IPVD, cirrhosis with IPVD without HPS, and cirrhosis with HPS;

[0022] Figure 1C The Kruskal-Wallis rank sum test was used to compare serum PDGF-BB concentrations among multiple groups in patients without IPVD, cirrhosis with IPVD without HPS, and cirrhosis with HPS;

[0023] Figure 2A AUC curves for predicting IPVD for different parameters;

[0024] Figure 2B AUC curves for predicting HPS for different parameters;

[0025] Figure 3ATo describe the correlation between clinical parameters and biomarkers, the heat map shows the Spearman correlation coefficient r of patient samples;

[0026] Figure 3B Heatmap depicting the correlation between clinical parameters and biomarkers, with the corresponding P value for each correlation coefficient r;

[0027] Figure 4A A nomogram for the IPVD prediction model in patients with cirrhosis that integrates four clinical parameters and three biomarkers;

[0028] Figure 4B This is the ROC curve prediction diagram for patients with cirrhosis combined with IPVD;

[0029] Figure 4C is the calibration curve of the nomogram used to predict IPVD;

[0030] Figure 4D It is a decision curve analysis diagram;

[0031] Figure 5A This is a nomogram for the HPS prediction model in patients with cirrhosis;

[0032] Figure 5B ROC curve diagram for prediction of HPS in patients with cirrhosis;

[0033] Figure 5C is the calibration curve graph of the nomogram used to predict HPS;

[0034] Figure 5D It is a decision curve analysis diagram;

[0035] Figure 6 is the AUC graph of each marker group in IPVD;

[0036] Figure 7 Figure 3 is the AUC graph of each marker group in HPS. DETAILED DESCRIPTION

[0037] To provide a deeper understanding of the present invention, a more detailed description will be provided below with reference to the accompanying drawings. The accompanying drawings illustrate preferred embodiments of the present invention, but it should be noted that the present invention is not limited to these specific embodiments. These embodiments are provided to help the public more fully understand the present invention, and are not intended to limit the scope of the present invention.

[0038] In the specification of the present invention, all technical and scientific terms, unless otherwise defined, should be interpreted according to the meaning commonly understood by those skilled in the art. The use of these terms is only for describing specific embodiments and is not intended to limit the scope of protection of the present invention.

[0039] 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 that contain these features. In the case of numerical intervals, unless otherwise specified, these numerical intervals are considered to be 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.

[0040] The numerical ranges disclosed herein may be combined with any lower and upper limits to form new ranges. Each individually disclosed point or value may also serve as the lower or upper limit of a new range. The temperature parameters herein, unless otherwise specified, may be either constant temperature or variable within a specific temperature range. Temperature fluctuations within the instrument's control accuracy are permitted.

[0041] In the description of the present invention, "multiple" refers to at least two, such as two or three, unless otherwise clearly defined. All embodiments of the present invention and their optional embodiments, as well as all technical features and optional technical features, can be combined with each other to form new technical solutions. 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 existing technologies or purchased through commercial channels.

[0042] This study has developed a predictive model for hepatopulmonary syndrome (HPS) in patients with cirrhosis based on clinical parameters and blood biomarkers. Compared to the current gold standard for diagnosing HPS, the model provided by this invention demonstrates superior sensitivity and specificity. This improvement makes the model valuable for clinical screening and serial assessment of HPS.

[0043] During the development of this predictive model, screening and evaluation identified a panel of novel biomarkers that may be used to distinguish between patients with cirrhosis and those with HPS. This panel includes S1P, angiopoietin-2 (Ang-2), and PDGF-BB. The combination of these biomarkers has shown potential clinical application in identifying the presence or absence of HPS in patients with cirrhosis.

[0044] Example 1 Screening for markers most closely related to the occurrence and development of IPVD and HPS and early joint detection of HPS Method for establishing diagnostic model

[0045] (1) 465 patients with cirrhosis admitted to Capital Medical University You'an Hospital between April 2023 and May 2024 were screened. Demographic data, clinical information, and laboratory parameters of the patients were collected through the electronic medical record system. Blood samples of eligible patients were retained for biomarker testing.

[0046] (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.

[0047] (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.

[0048] Three biomarkers were measured using enzyme-linked immunosorbent assays: 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 additional cytokines, 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), were measured using a customized ProcartaPlex 6-plex assay. Data were read on a Luminex 200 instrument (Luminex Corporation).

[0049] (4) In the process of model construction, the present invention uses the LASSO regression method to screen key modeling factors. LASSO regression is a statistical technique that can reduce model overfitting by introducing a penalty term to reduce model complexity. 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.

[0050] To evaluate the classification and diagnostic capabilities of the model, the present invention used a receiver operating characteristic (ROC) curve. The ROC curve is a graphical tool used to display the performance of a model at different threshold settings. Model performance is quantified by the area under the curve (AUC), with higher AUC values ​​indicating stronger diagnostic ability. Furthermore, the optimal cutoff value was determined using the Youden index, a statistic that comprehensively considers sensitivity and specificity to find the optimal balance.

[0051] The present invention also lists other performance indicators of the model in detail, including cut-off value, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). These indicators provide a comprehensive assessment of the diagnostic performance of the model.

[0052] To further analyze the relationship between different clinical parameters and biomarkers, this paper uses heat maps 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. This method can intuitively identify which parameters are strongly associated with the occurrence of HPS.

[0053] Finally, the present invention constructed a nomogram by integrating the seven identified predictors, a graphical tool for displaying risk profiles for individual patients. This nomogram can help physicians and researchers intuitively assess a patient's risk of HPS based on their clinical parameters and biomarker levels.

[0054] Example 2 Screening and analysis of markers most closely related to the occurrence and development of IPVD and HPS

[0055] After screening, a total of 320 patients were included in the analysis, of whom 101 met the diagnosis of IPVD, 54 of whom were diagnosed with HPS. Statistical analysis of biomarkers in hospitalized patients with cirrhosis (without IPVD, with IPVD but without HPS, or with HPS) revealed statistically significant differences (P < 0.05) in five biomarkers: S1P, Ang-2, ICAM-1, PDGF-BB, and VEGF-A. These biomarkers, as shown in Table 1, suggest that these five markers may serve as early warning and detection markers for IPVD and HPS in patients with cirrhosis.

[0056] 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 not yet with 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.

[0057] Table 1 Biomarkers in hospitalized patients with cirrhosis with or without IPVD and HPS

[0058]

[0059] The patients were divided into a group without IPVD and a group with IPVD. The group with IPVD included patients with isolated IPVD and those ultimately diagnosed with HPS. Predictive markers were compared between the two groups. Results showed that S1P, Ang-2, ICAM-1, and PDGF-BB remained statistically significant (P < 0.05), while VEGF-A was not significantly different (Table 2). The patients were also divided into a group without HPS and a group with HPS (the group without HPS included both the group without IPVD and the group with isolated IPVD). Predictive markers were compared between the two groups. Results showed that S1P, Ang-2, ICAM-1, PDGF-BB, and VEGF-A were statistically significant (P < 0.05), as shown in Table 3.

[0060] Table 2 Biomarkers in hospitalized patients with cirrhosis without and with IPVD

[0061]

[0062] Table 3 Biomarkers in hospitalized patients with cirrhosis without and with HPS

[0063]

[0064] We used univariate logistic regression analysis to identify variables that predict intrapulmonary vasodilation (IPVD) and hepatopulmonary syndrome (HPS). First, we compared variables between the IPVD-free group and the IPVD-affected group. Among the variables with statistically significant differences between the two groups, the three biomarkers S1P, Ang-2, and PDGF-BB also showed statistically significant differences in univariate logistic regression analysis (P < 0.05). The results are detailed in Table 4. Similarly, when we compared the HPS-free group and the HPS-affected 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.

[0065] Table 4 Univariate logistic regression analysis of biomarkers predicting IPVD in patients with cirrhosis

[0066]

[0067] Table 5 Univariate Logistic Regression Analysis of HPS Predictive Biomarkers in Patients with Cirrhosis

[0068]

[0069] exist Figures 1A to 1C In this study, we compared the levels of three biomarkers (S1P, Ang-2, and PDGF-BB) among patients with cirrhosis: those without IPVD (labeled "Without IPVD"), those with IPVD but without HPS (labeled "Without HPS"), and those with HPS (labeled "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 levels between the group without IPVD and the group with IPVD but without HPS, or between the group with IPVD but without HPS and the group with HPS (see Figure 1A Ang-2 levels 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 PDGF-BB level was highest in the non-IPVD group and was statistically significantly different from the IPVD-without-HPS group and the HPS group. However, there was no statistically significant difference in the PDGF-BB level between the IPVD-without-HPS group and the HPS group (see Figure 1C These findings suggest that combined detection of S1P, Ang-2, and PDGF-BB could serve as a potential means of detecting and early warning of IPVD and early HPS.

[0070] We used LASSO logistic regression to identify 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 receiver operating characteristic (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.

[0071] Table 6 Predictive value of different parameters and combination models for predicting IPVD and HPS in patients with cirrhosis

[0072]

[0073] In this study, 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 values ​​are displayed, with a color map ranging from 0 to 1, with blue indicating the maximum P value and white indicating the minimum P value. 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. All P values ​​are less than 0.001, showing strong statistical correlation. In addition, the correlations between the six numerical parameters in the study were 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. For example, the correlation coefficient P values ​​between age and other parameters are all greater than 0.05.

[0074] Figure 4A and Figure 4BA prediction model for IPVD that integrates 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 indicate that the model can effectively assist clinicians in identifying cirrhotic patients with IPVD.

[0075] 5A to 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) ranged from 0.848 to 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.

[0076] Example 3 Verification of the sensitivity and specificity of each marker and screening and analysis of marker groups

[0077] To improve the accuracy of early warning for intrapulmonary vasodilation (IPVD) and hepatopulmonary syndrome (HPS), the inventors discovered that using a combination of markers can achieve higher sensitivity, specificity, and accuracy compared to single marker detection. Therefore, the inventors conducted an in-depth screening of marker combinations in the hope of identifying optimal combinations for the detection of IPVD and HPS. This screening process resulted in the identification of a panel of markers that demonstrated higher diagnostic performance for early warning of IPVD and HPS than single markers. The specific screening results are described below.

[0078] The sensitivity and specificity of each marker in the present invention were verified in IPVD. The AUC data are shown in Table 7, and the ROC information is shown in Table 8.

[0079] Table 7

[0080]

[0081] Table 8

[0082]

[0083] The sensitivity and specificity of each marker included in the present invention were verified in HPS. The AUC data are shown in Table 9, and the ROC information is shown in Table 10.

[0084] Table 9

[0085]

[0086] Table 10

[0087]

[0088] 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.

[0089] Further validation of the superior marker panel:

[0090] (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 panel description is as follows.

[0091] Marker group 1 (corresponding to Model 1 in the figure): S1P, Ang-2, PDGF-BB;

[0092] Marker group 2 (corresponding to Model 2 in the figure): S1P, Ang-2, ICAM-1;

[0093] Marker group 3 (corresponding to Model 3 in the figure): Ang-2, PDGF-BB, VEGF-A;

[0094] Marker group 4 (corresponding to Model 4 in the figure): Ang-2, ICAM-1, VEGF-A.

[0095] Table 11

[0096]

[0097] Table 12

[0098]

[0099] (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.

[0100] Marker group 1 (corresponding to Model 1 in the figure): S1P, Ang-2, PDGF-BB;

[0101] Marker group 2 (corresponding to Model 2 in the figure): S1P, Ang-2, ICAM-1;

[0102] Marker group 3 (corresponding to Model 3 in the figure): Ang-2, PDGF-BB, VEGF-A;

[0103] Marker group 4 (corresponding to Model 4 in the figure): Ang-2, ICAM-1, VEGF-A.

[0104] Table 13

[0105]

[0106] Table 14

[0107]

[0108] The above experiments demonstrate that the specific marker combination of S1P, Ang-2, and PDGF-BB demonstrates significant superiority in detecting IPVD and HPS, outperforming not only single markers but also other marker combinations. Therefore, due to its high efficacy in detecting IPVD, this marker combination can be considered a potential marker for early detection, early warning, and diagnosis of HPS.

Claims

1. The use of a substance for specifically detecting the expression level of a biomarker in a sample to be tested, characterized in that The biomarkers are S1P, angiopoietin-2 and PDGF-BB; the application is: Application in the preparation of products for detecting pulmonary vasodilation and / or hepatopulmonary syndrome.

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

3. Use of a substance for specifically detecting the expression level of a biomarker in a test sample in the preparation and diagnosis of a model for pulmonary vasodilation and hepatopulmonary syndrome, characterized in that: The biomarkers are S1P, angiopoietin-2 and PDGF-BB.

Images