Application of biomarker combination in construction of prediction model for evaluating stability of intracranial aneurysm

Abstract

The invention provides application of a biomarker combination in construction of a prediction model for evaluating intracranial aneurysm stability and new application of the biomarker combination as a detection reagent and a kit. According to the technical scheme, three factors are used as a biomarker combination to be applied to a risk assessment model, the biomarker combination is the factors including TF, PGK1 and ART3, a prediction model for evaluating the stability of the intracranial aneurysm is constructed based on the biomarkers, and the prediction model is used for evaluating the stability of the intracranial aneurysm by combining a series quality tag labeling technology, a high performance liquid chromatography grading technology and other frontier scientific and technological means. A plasma sample is analyzed and screened, the biomarker suitable for evaluating the unstable state of the intracranial aneurysm is found, the biomarker has the characteristics of high sensitivity and good specificity, the unstable state of the unruptured aneurysm is subjected to early evaluation, a basis is provided for individualized diagnosis and treatment of a patient, and the waiting time and the treatment pain of the patient are reduced.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the application of a combination of biomarkers in constructing a predictive model for evaluating the stability of intracranial aneurysms, which is suitable for early warning of unstable states and evaluation of intracranial arterial stability. Background Technology

[0002] With the development of medical imaging technology and the popularization of health check-ups, the number of incidentally discovered unruptured intracranial aneurysms is increasing year by year. According to relevant studies, the prevalence of intracranial aneurysms in the general population is approximately 3.6% to 7%. Subarachnoid hemorrhage caused by the rupture of an intracranial aneurysm can lead to a rapid increase in intracranial pressure, followed by transient global cerebral ischemia and primary or secondary brain injury. Therefore, this disease exhibits a high rate of disability and mortality. Follow-up of incidentally discovered unruptured intracranial aneurysms may not be able to predict rupture in a timely and accurate manner. Furthermore, both open craniotomy clipping and endovascular embolization are invasive treatments that carry the risk of serious complications such as neurological dysfunction and persistent headaches. Therefore, assessing the stability of unruptured intracranial aneurysms, predicting the risk of rupture, and selecting appropriate treatment strategies are significant challenges in the diagnosis and treatment of unruptured intracranial aneurysms.

[0003] Previous studies suggested that small intracranial aneurysms with a diameter of less than 7 mm had an extremely low rupture rate. However, a meta-analysis of large datasets revealed that the average diameter of ruptured intracranial aneurysms was 6.28 mm, with 71.8% of these aneurysms being less than 7 mm in diameter. This demonstrates that simply using the diameter of an intracranial aneurysm to rule out the risk of rupture is inaccurate. Currently, internationally established clinical scoring models for assessing the instability of unruptured aneurysms include the PHASES aneurysm risk score (P: race, H: hypertension, A: age, S: aneurysm size, E: previous aneurysmal subarachnoid hemorrhage, S: aneurysm location), the ELAPSS scoring method (E: previous subarachnoid hemorrhage, L: aneurysm location, A: age 60 years or older, P: population, S: aneurysm size, S: aneurysm shape), and the unruptured intracranial aneurysm treatment scoring method. These three scoring methods have different endpoints, scoring based on factors such as age, race, aneurysm location, size and shape, history of hypertension, and history of subarachnoid hemorrhage, each with its own advantages and disadvantages. The PHASES aneurysm risk score uses aneurysm rupture as the endpoint and defines aneurysms smaller than 7 mm as low risk. This means it cannot accurately assess small aneurysms, which account for more than 70% of all aneurysms. The ELPASS score uses aneurysm growth as the endpoint, but the factors involved are relatively simple, and it also has the shortcoming of not being able to accurately assess small intracranial aneurysms. The treatment score for unruptured intracranial aneurysms uses treatment decision as the endpoint, which includes 29 variables with both positive and negative values. This may make it too complex and difficult to use widely.

[0004] The development of intracranial aneurysms involves complex interactions among multiple cellular and molecular components. The triggering factor for intracranial aneurysm formation may be hemodynamic stress inducing local inflammatory infiltration, thereby weakening the vessel wall. During aneurysm formation, abnormal blood flow leads to excessive mechanical load, resulting in damage to the internal elastic lamina and endothelial dysfunction. Hemodynamic abnormalities and endothelial cell inflammation trigger phenotypic transformation of smooth muscle cells, leading to extracellular matrix degradation and remodeling, ultimately resulting in aneurysm rupture. Pathological changes on the intracranial aneurysm wall inevitably affect the expression of exosomes in peripheral blood. By using high-throughput proteomics analysis to compare and screen differences in peripheral blood protein expression among patients, we can identify highly sensitive and specific biomarkers for assessing intracranial aneurysm instability, providing a reliable reference for evaluating the timing of intracranial aneurysm surgery. Summary of the Invention

[0005] The purpose of this invention is to provide an application of a combination of biomarkers in constructing a predictive model for evaluating the stability of intracranial aneurysms. This invention addresses the shortcomings of existing methods for assessing the risk of unstable transition of unruptured intracranial aneurysms and the lack of accurate biomarker application assessment models. It combines three factors as biomarkers in a risk assessment model and provides new uses for detection reagents and kits.

[0006] The technical solution of this invention is: the application of a combination of biomarkers in constructing a predictive model for the stability of intracranial aneurysms. The key technical points are: the combination of biomarkers consists of factors: TF, PGK1, and ART3, and a predictive model for evaluating the stability of intracranial aneurysms is constructed based on the biomarkers.

[0007] The formula for the prediction model is as follows: ln p / (1-p) = (-2.897)*C TF + 0.028*C PGK1 +(-0.145)*C ART3 + 10.754 C TF C PGK1 and C ART3 The values ​​are composed of the TF, PGK1, and ART3 concentrations in the peripheral blood plasma of the sample, respectively. The values ​​are assigned to specific measured values, and the probability p of the intracranial aneurysm instability risk of the sample is calculated.

[0008] The present invention also provides an application of the construction of the biomarker combination to evaluate the stability of intracranial aneurysms in a detection reagent. The key technical point is that the detection reagent is composed of a reagent for detecting the plasma expression levels of the biomarker combination factors TF, PGK1 and ART3.

[0009] The present invention also provides an application of the construction of the biomarker combination to evaluate the stability of intracranial aneurysms in a kit. The key technical point is that the detection reagent of the kit is composed of reagents for detecting the plasma expression levels of the biomarker combination factors TF, PGK1 and ART3.

[0010] This invention also provides an application method for constructing a predictive model for evaluating the stability of intracranial aneurysms using the aforementioned biomarker combination. The key technical points are: establishing a nomogram based on the biomarker combination in constructing the predictive model for evaluating the stability of intracranial aneurysms; evaluating the probability of instability risk through scoring the nomogram; setting up numerical axes for plasma TF concentration range, plasma PGK1 concentration range, and plasma ART3 concentration range, a risk score axis, a total risk score axis, and an unstable risk probability axis; taking the risk score values ​​corresponding to each plasma concentration value of TF, PGK1, and ART3 on the risk score axis, calculating the sum of each risk score value, finding the corresponding total risk score value on the total risk score axis, and finally finding the corresponding risk probability on the unstable risk probability axis.

[0011] The advantages and positive effects of this invention are: (1) At present, the clinical assessment of the unstable state of intracranial small aneurysms is still in a state of lacking accurate early warning biomarkers. This invention combines a variety of cutting-edge scientific and technological means such as tandem mass tagging technology, high performance liquid chromatography fractionation technology, and liquid chromatography-mass spectrometry to perform high-throughput proteomics analysis on plasma samples. By screening differential proteins, biomarkers suitable for evaluating the unstable state of intracranial aneurysms are discovered.

[0012] (2) This invention uses non-invasive peripheral blood collection of plasma samples to conduct early assessment of the unstable state of unruptured aneurysms, providing a basis for individualized diagnosis and treatment of patients and reducing patients' waiting time and treatment pain.

[0013] (3) This invention uses healthy people as a reference and patients with traumatic subarachnoid hemorrhage as disease controls. It compares the differences in plasma protein expression between patients with asymptomatic unruptured intracranial aneurysms and patients with ruptured intracranial aneurysms, screens out biomarkers related to the unstable state of intracranial aneurysms, and successfully obtains an intracranial aneurysm stability evaluation model.

[0014] (4) The predictive model constructed from biomarkers disclosed in this invention has the characteristics of high sensitivity and good specificity, with an area under the ROC curve of 0.955 (95% confidence interval: 0.907-1.000). It has a sensitivity of 96.8% and a specificity of 82.6% in distinguishing between ruptured and unruptured intracranial aneurysms. The predictive model has an accuracy of 82.6% in predicting the unstable state of unruptured aneurysms, which is significantly better than the PHASES score, ELPASS score, and treatment score for unruptured intracranial aneurysms (positive predictive rates of 56.5%, 43.5%, and 43.5%, respectively). Attached Figure Description

[0015] Figure 1This is a screening diagram of differentially expressed proteins in each group according to the present invention; Figure 2 This is a plasma expression level diagram of the TF group in this invention; Figure 3 This is a plasma expression level diagram of the PGK1 group in this invention; Figure 4 This is a plasma expression level diagram of the ART3 group of the present invention; Figure 5 This is a nodal plot of the prediction model of the present invention; Figure 6 This is the ROC curve of the prediction model of this invention. Detailed Implementation

[0016] This invention provides a novel use of biomarker combinations in constructing predictive models for evaluating the stability of intracranial aneurysms, specifically, their application in constructing predictive models for evaluating the stability of intracranial aneurysms. The invention is further described in detail below with reference to specific embodiments. Unless otherwise specified, the materials, reagents, pharmaceuticals, software systems, etc., used in the embodiments are commercially available.

[0017] The biomarker combination used consisted of three factors: TF, PGK1, and ART3, where TF is serum transferrin, PGK1 is phosphoglycerate kinase 1, and ART3 is ecto-ADP-ribosyltransferase 3. A predictive model for evaluating the stability of intracranial aneurysms was constructed based on these biomarkers.

[0018] The screening of biomarkers in this invention includes the following steps: I. Experimental Methods 1. Experimental Grouping

[0019] This study included five groups: ruptured intracranialaneurysm (RIA), symptomatic unruptured intracranialaneurysm (sUIA), asymptomatic unruptured intracranialaneurysm (aUIA), traumatic subarachnoid hemorrhage (tSAH), and healthy controls (HC).

[0020] 2. Inclusion criteria

[0021] (1) Inclusion criteria for patients with intracranial aneurysms: The presence of intracranial aneurysms was confirmed by the results of cranial computed tomography angiography and / or digital subtraction angiography; the aneurysm diameter was less than 7 mm.

[0022] (2) Exclusion criteria for patients with intracranial aneurysms: under 18 years of age; subarachnoid hemorrhage caused by non-aneurysmal factors; aneurysm diameter greater than 7 mm; diagnosed as dissecting aneurysm; suffering from other systemic diseases, malignant tumors or autoimmune diseases; onset time more than 24 hours or having undergone craniotomy, interventional or other surgical treatments.

[0023] (3) Based on the results of computed tomography or lumbar puncture in patients with intracranial aneurysms, determine whether the aneurysm has ruptured and divide them into RIA group and UIA group.

[0024] (4) Based on whether UIA patients have experienced prodromal symptoms such as headache, dizziness, cognitive impairment or visual impairment, patients are divided into sUIA group and aUIA group.

[0025] (5) Inclusion criteria for tSAH patients: subarachnoid hemorrhage caused by trauma; confirmed by head and neck vascular imaging examination that there is no intracranial aneurysm.

[0026] (6) Inclusion criteria for HC population: All physiological and laboratory tests are within the normal range; voluntary selection of head and neck vascular imaging examination and confirmation that there is no intracranial aneurysm.

[0027] 3. Sample collection

[0028] All participants underwent venous blood sampling (5 ml) within 2 hours of physical examination or hospital admission. The samples were collected in anticoagulant blood collection tubes, centrifuged at 3500 rpm for 10 minutes at 4 degrees Celsius, and the plasma was aliquoted into 0.2 ml tubes and stored at -80 degrees Celsius until analysis.

[0029] 4. Tandem mass labeling for relative and absolute quantification and liquid chromatography-tandem mass spectrometry analysis

[0030] 0.2 ml plasma samples were collected from each patient, and 10 patients were pooled into one pooled plasma sample. Each group consisted of 3 pooled samples. Tandem mass-tagged plasma proteomics analysis was performed on the RIA, UIA, tSAH, and HC groups.

[0031] Sample lysis and protein extraction were performed using a total protein extraction lysis buffer. Protein concentration was determined using a bispyridine carboxylic acid protein assay kit. Trypsin digestion was performed based on the degree of sample preparation aided by filtration. The digested peptides were desalted using a mixed cation exchange solid-phase extraction column, vacuum-centrifuged and dried, and then reconstituted with 40 μL of 0.1% formic acid solution. 100 μg of peptide mixture from each sample was labeled with a tandem mass tagging kit, homogenized, and then separated using a high-pH reverse-phase peptide separation kit. The peptide mixture was recombined and acidified with 0.1% trifluoroacetic acid solution, and then loaded onto a balanced, high-pH reverse-phase fractionating column. Desalting was first performed by low-speed centrifugation with water washing, followed by elution with an acetonitrile concentration gradient. Finally, the collected fractions were dried under vacuum and lyophilized with 12 μL of 0.1% formic acid solution.

[0032] Liquid chromatography-tandem mass spectrometry (LC-MS / MS) was performed using an orbital trap mass spectrometer. Loading was performed in buffer A (0.1% formic acid aqueous solution), followed by linear gradient separation in buffer B (84% acetonitrile and 0.1% formic acid) at a flow rate of 300 nanoliters per minute. The mass spectrometer operated in positive ion mode. Mass spectrometric data were processed using the top 20 data-dependent methods, dynamically selecting the most abundant precursor ions from the survey scans (mass-to-charge ratio 300–1800) for high-energy collisional dissociation fragmentation. The automatic gain control target was set to 1e6, the maximum injection time was 50 ms, and the dynamic exclusion time was 30.0 seconds. The resolution of the measurement scans was 60,000 mass-to-charge ratio 200, the resolution of the high-energy collisional dissociation spectra was set to 15,000 mass-to-charge ratio 200, and the isolation width was 1.5 mass-to-charge ratio. The normalized collision energy was 30 eV, and the bottom fill ratio was defined as 0.1%.

[0033] 5. Protein identification and quantification

[0034] Proteins were identified and quantified using Proteome Discoverer 2.4 software, which incorporates an embedded matrix scientific peptide fingerprint search engine (version 2.2). The ratio of protein expression levels in the RIA, UIA, and tSAH groups to those in the HC group was used as the fold change. Proteins were considered differentially expressed when the fold change was >1.2-fold or <0.83-fold, and P < 0.05.

[0035] 6. Enzyme-linked immunosorbent assay (ELISA)

[0036] Plasma protein concentration was determined using enzyme-linked immunosorbent assay (ELISA). The test reagents were purchased from Shanghai Enzyme-Linked Biotechnology Co., Ltd., and the test process was strictly carried out in accordance with the instructions.

[0037] 7. Statistical methods

[0038] Statistical analysis and graphing were performed using SPSS 23.0 and the rms and pROC packages in R version 4.2.2. Quantitative data were tested for normality using the Kolmokolov-Smilov test. Normally distributed quantitative data were expressed as mean ± standard deviation, and comparisons between groups were performed using Student's t-test or one-way ANOVA. Non-normally distributed quantitative data were expressed as median (interquartile range), and comparisons between groups were performed using the Mann-Whitney rank-sum test or the Kruskal-Wallis test. Categorical data were expressed as percentages, and comparisons between groups were performed using the chi-square test or Fisher's exact test. Receiver operating characteristic (ROC) curves were used to calculate the predictive power of variables, estimating the area under the curve (AUC) and the corresponding 95% confidence intervals. The statistical significance level was set at 0.05.

[0039] 8. Construction of nomogram for evaluating the stability of intracranial aneurysms

[0040] The odds ratios of the 95% confidence intervals were calculated using univariate and multivariate logistic regression to exclude confounding variables. Aneurysmal instability-related variables with p < 0.05 in the univariate logistic regression analysis were included in the discussion of the multivariate regression analysis. If these factors still maintained a significant correlation with p < 0.05, they were included in the predictive model, and then nomograms were constructed using these variables.

[0041] II. Experimental Results

[0042] 1. Analysis of results from tandem mass labeling analysis combined with liquid chromatography-mass spectrometry (LC-MS) A total of 1,050,799 secondary spectra were obtained by tandem mass tag analysis combined with liquid chromatography-mass spectrometry, of which 34,699 were database-matched; 7,053 were unique peptides; and 1,070 proteins were identified, of which 1,069 were quantifiable.

[0043] 2. Screening of differentially expressed proteins associated with intracranial aneurysm instability like Figure 1 As shown, proteins exhibiting significant changes only in the RIA or UIA groups were selected as differentially expressed proteins. If a protein showed a significant change in the same trend as in the RIA or UIA group in the tSAH control group, it was considered not a specific differentially expressed protein. According to the Venn diagram results, 26 differentially expressed proteins showed significant changes only in RIA patients, with 13 proteins showing significantly increased expression and 13 proteins showing significantly decreased expression, such as... Figure 1 As shown in region ab. Seven differentially expressed proteins showed significant changes only in UIA patients, with three proteins showing significantly increased expression and four proteins showing significantly decreased expression, such as... Figure 1 As shown in the cd region.

[0044] Analysis of the results of tandem mass tagging analysis combined with liquid chromatography-mass spectrometry showed that TF, PGK1, and ART3 were differentially expressed proteins with significant changes in relative expression levels only in the plasma of patients in the RIA group.

[0045] 3. Expression of TF, PGK1 and ART3 in patient plasma

[0046] The concentrations of plasma biomarkers in each group were detected using an enzyme-linked immunosorbent assay (ELISA), and the results are as follows: Figures 2 to 4 As shown.

[0047] like Figure 2 As shown, the mean plasma TF concentration in the RIA group was (2.06±0.50) ng / ml, the mean plasma TF concentration in the UIA group was (4.88±2.44) ng / ml, the mean plasma TF concentration in the traumatic subarachnoid hemorrhage control group was (6.53±1.60) ng / ml, and the mean plasma TF concentration in the healthy control group was (7.03±2.42) ng / ml. One-way ANOVA showed that the plasma TF concentration in the rupture group was significantly different from the other three groups (P<0.001). The TF concentration in the rupture group was significantly lower than that in the unruptured group, which was slightly lower than that in the tSAH control group and the HC group (P<0.001 and P=0.005, respectively).

[0048] like Figure 3 As shown, the mean plasma PGK1 concentration in the RIA group was (507.51±252.41) pg / ml, the mean plasma PGK1 concentration in the UIA group was (255.55±97.36) pg / ml, the mean plasma PGK1 concentration in the traumatic subarachnoid hemorrhage control group was (282.55±77.49) pg / ml, and the mean plasma PGK1 concentration in the healthy control group was (299.87±62.96) pg / ml. One-way ANOVA showed that the plasma PGK1 concentration in the rupture group was significantly different from the other three groups (P<0.001), with a significantly higher PGK1 concentration in the rupture group. There were no significant differences between the unruptured group and the tSAH control group and the HC group (P=0.390 and P=0.600, respectively).

[0049] like Figure 4As shown, the mean plasma ART3 concentration was (92.22±14.10) ng / ml in the RIA group, (76.57±21.71) ng / ml in the UIA group, (68.73±14.25) ng / ml in the tSAH group, and (65.52±16.66) ng / ml in the HC group. One-way ANOVA showed that the plasma ART3 concentration in the RIA group was significantly different from the other three groups (P<0.001). The ART3 concentration in the RIA group was significantly higher than that in the UIA group, which was slightly higher than that in the tSAH control group and the HC group (P=0.144 and P=0.041, respectively).

[0050] 4. Peripheral blood plasma levels of TF, PGK1, and ART3 are independent risk factors for unstable intracranial aneurysms.

[0051] The following is a list of risk factors associated with intracranial aneurysm instability, analyzed by univariate and multivariate logistic regression models:

[0052] Univariate logistic regression analysis showed that the hazard ratio (ORR) of plasma TF levels to intracranial aneurysm instability was 0.197 (95% confidence interval: 0.049–0.788, P = 0.022), plasma PGK1 levels were 1.012 (95% confidence interval: 1.005–1.020, P = 0.001), and plasma ART3 levels were 1.053 (95% confidence interval: 1.013–1.094, P = 0.009). Incorporating these indicators into a multivariate logistic regression model, the results showed that TF, PGK1, and ART3 were all independent risk factors for intracranial aneurysm instability.

[0053] 5. Construction, use, and validation of nomogram models

[0054] Based on the logistic regression results, we jointly established a model for clinical assessment of intracranial aneurysm instability using independent risk factors (including TF plasma concentration, PGK1 plasma concentration, and ART3 plasma concentration) from multivariate logistic regression analysis, and visualized it using a nomogram.

[0055] Assuming y=1 indicates an unstable aneurysm and y=0 indicates a stable aneurysm, logistic regression is used to determine whether the aneurysm is stable or unstable, and the probability of this outcome. The probability of instability is P, then the probability of stability is 1-P. Based on the general form of multivariate logistic regression: logit(P)=lnP / (1-P)=β0+β1x1+β2x2+…+β m x m The entire model uses the maximum likelihood method for parameter estimation. The logistic regression module, including but not limited to SPSS, R, and SPSSAU online calculation software, is used to obtain the risk ratios of plasma TK, PGK1, and ART3 levels to intracranial aneurysm instability. Then, β... n =ln(risk ratio) n The specific calculation results are shown in the table below:

[0056] The formula for calculating the risk probability p obtained from the prediction model is as follows: ln p / (1-p) = (-2.897)*C TF + 0.028*C PGK1 +(-0.145)*C ART3 + 10.754 C TF C PGK1 and C ART3 All values ​​represent the concentration of plasma protein expression in patient samples, and are assigned specific measured values.

[0057] The stability of intracranial aneurysms is evaluated based on the concentrations of TF, PGK1, and ART3 in plasma. The concentrations of TF, PGK1, and ART3 proteins in the plasma samples of the subjects are detected, and the specific evaluation method is a predictive model and its expression formula.

[0058] like Figure 5 As shown, the above model is visualized in the form of a nomogram: The application method of constructing a predictive model for evaluating the stability of intracranial aneurysms using biomarker combinations involves establishing a nomogram based on the assessment model of unstable intracranial aneurysms constructed using the biomarker combinations. The nomogram is used to score and evaluate the probability of unstable state risk. The following axes are set: plasma TF concentration range, plasma ART3 concentration range, plasma PGK1 concentration norm, risk score axis, total risk score axis, and unstable state risk probability axis. Risk scores are taken for each plasma concentration value of TF, ART3, and PGK1 on the corresponding risk score axis. The sum of each risk score value is calculated, and then the corresponding sum of risk scores is found on the total risk score axis. Finally, the corresponding risk probability is found on the unstable state risk probability axis.

[0059] A plasma TF level of 0 ng / ml corresponds to a risk score of 75; a plasma TF level of 10 ng / ml corresponds to a risk score of 0. A plasma PGK1 level of 0 ng / ml corresponds to a risk score of 0; a plasma PGK1 level of 1400 ng / ml corresponds to a risk score of 100. A plasma ART3 level of 110 ng / ml corresponds to a risk score of 0; a plasma ART3 level of 30 ng / ml corresponds to a risk score of 30. When using this method, based on the specific plasma concentration, move upwards to the first row, the "Risk Score" axis, to find the corresponding risk score. Calculate the sum of the scores for each factor. Find the total risk score in the "Total Risk Score" row. A total risk score of 76 corresponds to a risk probability of 0.01; a total risk score of 88 corresponds to a risk probability of 0.5; and a total risk score of 100 corresponds to a risk probability of 0.99. Move downwards to the "Unstable State Risk Probability" row to find the corresponding risk probability for patients with intracranial aneurysms experiencing instability.

[0060] like Figure 6 As shown, the ROC curve analysis model distinguishes between RIA and UIA, with a mean area under the curve of 0.955 (95% confidence interval: 0.907-1.000), sensitivity of 96.8%, and specificity of 82.6%. The application of this invention in UIA patients to assess unstable states showed a positive predictive value of 100% and an accuracy of 82.6%, indicating excellent distinguishability.

[0061] This invention also provides the application of the aforementioned biomarker combination in a detection reagent for constructing a predictive model for evaluating the stability of intracranial aneurysms. The detection reagent consists of a reagent for detecting the plasma expression levels of the biomarker combination factors TF, PGK1, and ART3. The detection reagent includes: three pre-coated ELISA plates, respectively coated with human monoclonal anti-TF antibody, human monoclonal anti-PGK1 antibody, and human monoclonal anti-ART3 antibody; horseradish peroxidase-labeled rabbit / sheep anti-TF detection antibody, anti-PGK1 detection antibody, and anti-ART3 detection antibody; and three sets of gradient standards, namely, human recombinant TF protein standard, human recombinant PGK1 protein standard, and human recombinant ART3 protein standard.

[0062] This invention also provides an application of the aforementioned biomarker combination in a kit for constructing a predictive model to evaluate the stability of intracranial aneurysms. The kit's detection reagents consist of reagents for detecting the plasma expression levels of the biomarker combination factors TF, PGK1, and ART3. These detection reagents are key components of the kit for implementing the intracranial aneurysm stability evaluation model and can utilize the aforementioned predictive model. The kit also includes: sample dilution buffer (phosphate buffer + 1% bovine serum albumin), 20-fold concentrated wash buffer (phosphate buffer + 1% Tween 20), tetramethylbenzidine chromogenic solution, sulfuric acid stop solution, and sealing adhesive tape. When used, a p-value > 0.5 indicates a high risk of intracranial aneurysm instability, and treatment is recommended.

[0063] This invention utilizes a biomarker group derived from high-throughput screening, including TF, PGK1, and ART3, to establish a comprehensive predictive model for early warning of intracranial aneurysm instability, reducing treatment risks and patient suffering. Four methods for predicting aneurysm instability were evaluated in a selected population of intracranial aneurysm patients: the PHASES score, the ELPASS score, the unruptured intracranial aneurysm treatment score, and the predictive model of this invention. Results showed that the accuracy rate of the PHASES score was 56.5%, the ELPASS score was 43.5%, the unruptured intracranial aneurysm treatment score was 43.5%, and the predictive model's accuracy was 82.6%. This indicates that the model outperforms existing international clinical scoring standards. The candidate biomarkers were validated in a population, and independent predictors of intracranial aneurysm instability were identified through logistic regression analysis. The sensitivity and accuracy of the model were evaluated using ROC curves, ultimately demonstrating its success. The model is non-invasive, reliable, easy to detect and calculate, and suitable for widespread application.

[0064] In summary, the objective of this invention has been achieved.

Claims

1. The application of a combination of biomarkers in constructing a predictive model for evaluating the stability of intracranial aneurysms, characterized in that: The biomarker combination consists of factors: TF, PGK1, and ART3. A predictive model for evaluating the stability of intracranial aneurysms is constructed based on these biomarkers.

2. The application of the prediction model according to claim 1, characterized in that: The formula for the prediction model is as follows: ln p / (1-p) = (-2.897)*C TF + 0.028*C PGK1 +(-0.145)*C ART3 + 10.754 C TF C PGK1 and C ART3 The values ​​are composed of the TF concentration, PGK1 concentration, and plasma ART3 concentration of the sample plasma, respectively. The values ​​are assigned to specific measured values, and the probability p of the aneurysm instability state risk of the sample is calculated.

3. The application of a predictive model for evaluating the stability of intracranial aneurysms, constructed from a combination of biomarkers, in a detection reagent, characterized in that: The test reagent consists of reagents for detecting the plasma expression levels of the biomarker combination factors TF, PGK1, and ART3.

4. The application of a biomarker combination construction method for evaluating the stability of intracranial aneurysms in a reagent kit, characterized in that: The kit's detection reagents consist of reagents for detecting the plasma expression levels of the biomarker combination factors TF, PGK1, and ART3.

5. A method for applying the prediction model according to claim 1 or 2, characterized in that: Based on the combination of biomarkers, a predictive model for evaluating the stability of intracranial aneurysms was constructed, and a nomogram was established. The probability of unstable state risk was evaluated by scoring the nomogram. Set up the plasma TF concentration range, plasma PGK1 concentration range, and plasma ART3 concentration norm axis, risk score axis, total risk score axis, and unstable state risk probability axis respectively. Take the risk score value corresponding to each plasma concentration value of TF, PGK1, and ART3 on the risk score axis, calculate the sum of each risk score value, find the corresponding total risk score value on the total risk score axis, and finally find the corresponding risk probability on the unstable state risk probability axis.

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