Application of biomarker combination in intracranial aneurysm stability evaluation model

Abstract

The invention provides an application of a biomarker combination in an intracranial aneurysm stability assessment model and a new application of the biomarker combination as a reagent and a kit.The technical scheme is that three factors are used as the biomarker combination to be applied to a risk assessment model, the biomarker combination is the factors of PYGL, GSTP1 and STC2, and the factors of PYGL, GSTP1 and STC2 are the factors of PYGL, GSTP1 and STC2. An intracranial aneurysm stability evaluation model is constructed based on the biomarker, a new intracranial aneurysm stability evaluation model is formed, the current intracranial aneurysm diagnosis and treatment pattern can be improved in a non-invasive, efficient and low-cost mode, the influence of subjective factors is reduced or reduced, a comprehensive and objective basis is provided for clinical decision making, and the clinical application prospect is wide. The unstable state of the unruptured aneurysm is evaluated in an early stage, a basis is provided for individualized diagnosis and treatment of a patient, and the waiting time and 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 an intracranial aneurysm stability assessment model. Background Technology

[0002] Intracranial aneurysms are abnormal, pathological cystic dilatations of the walls of cerebral arteries, typically located at the bifurcation of the circle of arteries at the base of the brain. The incidence of intracranial aneurysms in the general population is approximately 3-5%, with about a quarter eventually rupturing. Subarachnoid hemorrhage caused by ruptured intracranial aneurysms is a highly destructive cerebrovascular disease; about a quarter of patients die before hospitalization, with a mortality rate of approximately 40% for the first hemorrhage, and 50% of survivors experiencing disability. While preventative treatment before rupture can improve survival rates and reduce the risk of subarachnoid hemorrhage, complications still occur in 6% of patients, and the cost of treatment and the utilization of medical resources become a burden on society and families. Therefore, assessing the instability of unruptured intracranial aneurysms to inform medical staff about treatment intervention or conservative treatment decisions, and making individualized treatment choices, is crucial.

[0003] Currently, the diagnosis and treatment of intracranial aneurysms mainly rely on cerebral angiography, magnetic resonance angiography, and CT angiography. All three methods are expensive, and cerebral angiography, the gold standard, is invasive, deterring many patients and their families. The influencing factors related to intracranial aneurysm rupture are diverse and complex, including age, sex, hypertension, family history, the ratio of aneurysm length to diameter, and the ratio of aneurysm length to neck width. This often leads to subjective judgment in clinical decision-making, lacking an objective, standardized, and stable evaluation system. Therefore, there is an urgent clinical need for a set of plasma biomarkers for intracranial aneurysms to form a new assessment model for intracranial aneurysm stability. This model could non-invasively, efficiently, and cost-effectively improve the current landscape of intracranial aneurysm diagnosis and treatment, reduce or minimize the influence of subjective factors, and provide a comprehensive and objective basis for clinical decision-making. Summary of the Invention

[0004] The purpose of this invention is to provide an application of a combination of biomarkers in an intracranial aneurysm stability assessment model, which solves the shortcomings of existing methods for assessing the unstable state of unruptured intracranial aneurysms and the lack of accurate intracranial aneurysm stability assessment models. It combines three factors as biomarkers in a risk assessment model and provides new uses for them as reagents and kits.

[0005] The technical solution of the present invention is: the application of a combination of biomarkers in an intracranial aneurysm stability assessment model. The key technical points are: the combination of biomarkers consists of factors: PYGL, GSTP1, and STC2, and an intracranial aneurysm stability assessment model is constructed based on the biomarkers.

[0006] The evaluation model is expressed by the following formula: ln p / (1-p) = 0.288*C PYGL + 0.253*C GSTP1 + 0.082*C STC2 + (-16.081) C PYGL C GSTP1 and C STC2 The abundance of PYGL, GSTP1, and STC2 in peripheral blood plasma of the sample are respectively assigned specific measured values, and the probability p of intracranial aneurysm instability risk of the sample is calculated.

[0007] The present invention also provides an application of the intracranial aneurysm stability assessment model of the biomarker combination 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 PYGL, GSTP1 and STC2.

[0008] The present invention also provides an application of the intracranial aneurysm stability assessment model of the biomarker combination 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 PYGL, GSTP1 and STC2.

[0009] This invention also provides a method for applying the intracranial aneurysm stability assessment model based on the aforementioned biomarker combination. The key technical points are: constructing an intracranial aneurysm stability assessment model based on the aforementioned biomarker combination and establishing a nomogram; evaluating the probability of instability risk through nomogram scoring; setting up numerical axes for plasma PYGL abundance range, plasma GSTP1 abundance range, and plasma STC2 abundance range, as well as a risk score axis, a total risk score axis, and an unstable risk probability axis; taking the risk score values ​​corresponding to the patient's plasma PYGL, GSTP1, and STC2 abundance values ​​on the risk score axis, calculating the sum of each risk score value, finding the corresponding total risk score value sum on the total risk score axis, and finally finding the corresponding risk probability on the unstable risk probability axis.

[0010] The advantages and positive effects of this invention are: (1) At present, there is still a lack of accurate biomarkers for evaluating the unstable state of intracranial aneurysms in clinical practice. This invention forms a new assessment model for the stability of intracranial aneurysms, which can improve the current diagnosis and treatment of intracranial aneurysms in a non-invasive, efficient and low-cost manner, reduce or reduce the influence of subjective factors, and provide a comprehensive and objective basis for clinical decision-making.

[0011] (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.

[0012] (3) The assessment 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 0.903 (95% confidence interval: 0.826-0.981). The sensitivity in distinguishing between ruptured and unruptured intracranial aneurysms is 77.4%, and the specificity is 87.0%. The accuracy of the assessment model in evaluating the unstable state of unruptured aneurysms is 78.3%, 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

[0013] Figure 1 This is a graph showing the PYGL plasma abundance levels in each group of patients according to the present invention. Figure 2 This is a graph showing the GSTP1 plasma abundance levels in each group of patients according to the present invention. Figure 3 This is a graph showing the STC2 plasma abundance levels in each group of patients according to the present invention. Figure 4 The ROC curve of plasma PYGL abundance level in assessing intracranial aneurysm instability is shown in the present invention. Figure 5 The ROC curve of plasma GSTP1 abundance level in assessing intracranial aneurysm instability is shown in the present invention. Figure 6 The ROC curve of plasma STC2 abundance level in assessing intracranial aneurysm instability is shown in the present invention. Figure 7 A nomogram is used to illustrate the scoring application of the evaluation model of this invention; Figure 8 This is the ROC curve of the evaluation model of this invention. Detailed Implementation

[0014] This invention provides a novel use of biomarker combinations in an intracranial aneurysm stability assessment model, specifically for constructing such a model. The invention is further described in detail below with reference to specific embodiments. Unless otherwise specified, all materials, reagents, pharmaceuticals, and software systems used in these embodiments are commercially available.

[0015] The biomarker combination used consisted of factors: PYGL, GSTP1, and STC2, where PYGL is glycogen phosphorylase L (liver form), GSTP1 is glutathione S-transferase P1, and STC2 is stanniocalcin-2. An assessment model for intracranial aneurysm stability was constructed based on these biomarkers.

[0016] The screening of biomarkers in this invention includes the following steps:

[0017] This study included five groups: ruptured intracranialaneurysm (RIA), symptomatic unruptured intracranialaneurysm (sUIA), asymptomatic unruptured intracranialaneurysm (aUIA), traumatic subarachnoid hemorrhage (tSAH), and healthy controls (HC).

[0018] 2. Inclusion criteria

[0019] (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.

[0020] (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.

[0021] (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.

[0022] (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.

[0023] (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.

[0024] (6) Inclusion criteria for HC population: All physiological and laboratory tests were within the normal range; voluntary selection of head and neck vascular imaging examination and confirmation of the absence of IAs.

[0025] 3. Sample collection

[0026] 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.

[0027] 4. Plasma proteomics identification based on data-independent acquisition mass spectrometry analysis

[0028] 0.2 ml of plasma was collected from each patient, and 10 patients were pooled into 1 pooled plasma sample. Each group consisted of 3 pooled samples. Plasma proteomics identification was performed on the RIA, UIA, tSAH, and HC groups based on data-independent acquisition mass spectrometry analysis.

[0029] Pre-wash 40 μL of magnetic beads twice with washing buffer, then add them to 50 μL of plasma, mix, and incubate on a magnetic rack for 1 hour. Then, discard the waste liquid and wash three times with washing buffer for 5 minutes each time.

[0030] For each sample, 0.5 μL of 1 mol / L tris(2-carboxyethyl)phosphohydrochloride and 2 μL of 1 mol / L chloroacetamide were added for reductive alkylation. After shaking and brief mixing, the sample was incubated at 60°C for 30 minutes. After incubation, the sample was cooled to room temperature, and 1 μg of trypsin and 0.5 μg of lysyl endopeptidase were added. After shaking and brief mixing, the sample was incubated at 37°C for 3 hours to obtain the enzymatically digested peptides. The peptides from each sample were desalted on a cation exchange solid-phase extraction column, concentrated by vacuum centrifugation, and then reconstituted with 20 μL of 0.1% formic acid. The peptide content was estimated by UV spectral density at 280 nm. For data-independent acquisition experiments, calibration peptides with calibrated retention times were added to the sample.

[0031] Peptides in each sample were analyzed by a mass spectrometer connected to a liquid chromatography system in data-independent acquisition mode. A 1.9 μm × 150 μm × 15 cm column was used at a flow rate of 800 nanoliters per minute. Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an aqueous solution containing 80% acetonitrile and 0.1% formic acid. Precursor ions were scanned within a mass-to-charge ratio range of 380–980. At a mass-to-charge ratio of 200, the first-stage mass spectrometry resolution was 240,000, the normalized automatic gain control target was 500%, and the maximum injection time was 5 ms. In the data-independent acquisition mode for the second-stage mass spectrometry scan, 299 scan windows were set, with an isolation window of 2 mass-to-charge ratio, a high-energy collision dissociation collision energy of 25 eV, a normalized automatic gain control target of 500%, and a maximum injection time of 3 ms.

[0032] 5. Protein identification and quantification

[0033] Data collected independently were analyzed using DIA-NN 1.8.1 software. The main software parameters were set as follows: trypsin as the enzyme, maximum missed cuts of 1, fixed modification as cysteine ​​aminomethylation, and dynamic modifications as methionine oxidation and protein N-terminal acetylation. All reported data were based on a 99% confidence level for protein identification, determined by a false discovery rate (FDR) ≤ 1%.

[0034] 6. Enzyme-linked immunosorbent assay (ELISA)

[0035] Plasma protein abundance 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.

[0036] 7. Statistical methods

[0037] 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 evaluative power of variables, and the area under the curve (AUC) and corresponding 95% confidence intervals were estimated. The statistical significance level was set at 0.05.

[0038] 8. Construction of a nomogram for assessing the stability of intracranial aneurysms

[0039] 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 evaluation model, and then nomograms were constructed using these variables.

[0040] II. Experimental Results

[0041] 1. Data-independent acquisition combined with liquid chromatography-mass spectrometry (LC-MS) was used to detect and screen differentially expressed proteins associated with intracranial aneurysm instability. A total of 1,050,799 secondary spectra were obtained through data-independent acquisition analysis combined with liquid chromatography-mass spectrometry (LC-MS), of which 34,699 were database-matched; 7,053 unique peptides were identified; and 1,070 proteins were identified, of which 1,069 were quantifiable. Analysis of the data-independent acquisition results combined with LC-MS showed that PYGL, GSTP1, and STC2 were differentially expressed proteins with significantly different relative expression levels only in the plasma of patients with recurrent acute arterial disease (RIA).

[0042] 2. Expression of PYGL, GSTP1, and STC2 in patient plasma

[0043] The abundance of plasma biomarkers in each group was detected by enzyme-linked immunosorbent assay (ELISA).

[0044] like Figure 1 As shown, the mean plasma PYGL abundance was (20.42±6.28) ng / ml in the RIA group, (14.43±3.49) ng / ml in the UIA group, (14.62±3.34) ng / ml in the tSAH group, and (14.63±4.31) ng / ml in the HC group. One-way ANOVA showed that plasma PYGL abundance was significantly increased in RIA patients, with significant differences compared to the other three groups (UIA: P<0.001, tSAH: P<0.001, HC: P<0.001); there were no significant differences between the UIA group, the tSAH control group, and the HC group (P=0.898, P=0.898).

[0045] like Figure 2As shown, the mean plasma GSTP1 abundance was (21.17±6.25) ng / ml in the RIA group, (15.88±2.88) ng / ml in the UIA group, (16.44±3.29) ng / ml in the tSAH group, and (16.64±3.85) ng / ml in the HC group. One-way ANOVA showed that the plasma GSTP1 abundance was significantly higher in the RIA group, with significant differences compared to the other three groups (UIA: P<0.001, tSAH: P<0.001, HC: P<0.001); there were no significant differences between the UIA group and the tSAH control group and the HC group (P=0.694 and P=0.595, respectively).

[0046] like Figure 3 As shown, the mean plasma STC2 abundance was (92.22±14.10) pg / ml in the RIA group, (76.57±21.71) pg / ml in the UIA group, (68.73±14.25) pg / ml in the tSAH group, and (65.52±16.66) pg / ml in the HC group. One-way ANOVA showed that the plasma STC2 abundance was significantly higher in the RIA group, with significant differences compared to the other three groups (UIA: P<0.001, tSAH: P<0.001, HC: P<0.001); there was no significant difference between the UIA group and the tSAH control group (P=0.060), but the UIA group was slightly higher than the healthy group (P=0.010).

[0047] 3. Peripheral blood plasma levels of PYGL, GSTP1, and STC2 are independent risk factors for intracranial aneurysm instability.

[0048] The following is a list of risk factors associated with intracranial aneurysm instability, analyzed by univariate and multivariate logistic regression models:

[0049] Univariate logistic regression analysis showed that the hazard ratio (ORR) of plasma PYGL level to intracranial aneurysm instability was 1.423 (95% confidence interval: 1.144–1.770, P = 0.002), the ORR of plasma GSTP1 level was 1.401 (95% confidence interval: 1.118–1.755, P = 0.003), and the ORR of plasma STC2 level was 1.119 (95% confidence interval: 1.047–1.197, P = 0.001). Incorporating these indicators into a multivariate logistic regression model, the results showed that PYGL, GSTP1, and STC2 were all independent risk factors for intracranial aneurysm instability.

[0050] 4. ROC curves of plasma PYGL, GSTP1, and STC2 levels in assessing intracranial aneurysm instability

[0051] Based on logistic regression results, we used ROC curves to validate the ability of plasma PYGL, GSTP1, and STC2 levels in assessing intracranial aneurysm instability.

[0052] like Figure 4 As shown, ROC curve analysis revealed that the area under the curve for PYGL was 0.815, the 95% confidence interval was 0.703–0.927, the significance level was P < 0.001, the critical value was 16.23 ng / ml, the sensitivity was 83.9%, and the specificity was 69.6%. Figure 5 As shown, the area under the curve for GSTP1 was 0.784, the 95% confidence interval was 0.664–0.904, the significance level was P = 0.004, the critical value was 19.18 ng / ml, the sensitivity was 51.6%, and the specificity was 95.7%. Figure 6 As shown, the area under the curve for STC2 was 0.868, the 95% confidence interval was 0.770-0.966, the significance level was P<0.001, the critical value was 89.62 pg / ml, the sensitivity was 74.2%, and the specificity was 95.7%.

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

[0054] Based on the logistic regression results, we jointly established a model for clinically assessing the instability of intracranial aneurysms using independent risk factors (including PYGL plasma abundance, GSTP1 plasma abundance, and STC2 plasma abundance) 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 was parameter-estimated using the maximum likelihood method. The logistic regression module of software including, but not limited to, SPSS, R, and SPSSAU online calculation was used to obtain the hazard ratios of plasma levels of PYGL, GSTP1, and STC2 to intracranial aneurysm instability. Therefore, β = ln(hazard ratio). The specific calculation results are shown in the table below:

[0056] The final formula used by the evaluation model to calculate the risk probability p is: ln p / (1-p) = 0.288*C PYGL + 0.253*C GSTP1 + 0.082*C STC2 + (-16.081) C PYGL C GSTP1 and C STC2 All values ​​represent the abundance of plasma protein expression in patient samples, and are assigned specific measured values.

[0057] The stability of intracranial aneurysms was evaluated based on the concentrations of PYGL, GSTP1, and STC2 in plasma. The concentrations of PYGL, GSTP1, and STC2 proteins in the plasma samples of the subjects were detected, and the specific evaluation method was a prediction model and its expression formula.

[0058] like Figure 7 As shown, the above model is visualized in the form of a nomogram: The application method of constructing an intracranial aneurysm stability assessment model using biomarker combinations involves establishing a nomogram based on the assessment model of unstable intracranial aneurysm states constructed using the biomarker combinations. The nomogram is used to score and evaluate the risk probability of unstable states. The following axes are set: plasma PYGL abundance range, plasma STC2 abundance range, plasma GSTP1 abundance norm, risk score axis, total risk score axis, and unstable state risk probability axis. Risk scores are taken for each plasma abundance value of PYGL, STC2, and GSTP1 on the corresponding risk score axis. The sum of each risk score value is calculated, and then the corresponding total risk score 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 PYGL level of 5 ng / ml corresponds to a risk score of 0; a plasma PYGL level of 40 ng / ml corresponds to a risk score of 100. A plasma GSTP1 level of 5 ng / ml corresponds to a risk score of 0; a plasma GSTP1 level of 45 ng / ml corresponds to a risk score of 100. A plasma STC2 level of 30 pg / ml corresponds to a risk score of 0; a plasma STC2 level of 110 pg / ml corresponds to a risk score of 65. When using this method, based on the specific plasma abundance, 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 62 corresponds to a risk probability of 0.01; a total risk score of 108 corresponds to a risk probability of 0.5; and a total risk score of 152 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 8 As shown, the ROC curve analysis model distinguishes between RIA and UIA, with a mean area under the curve of 0.903 (95% confidence interval: 0.826-0.981), sensitivity of 77.4%, and specificity of 87.0%. The application of this invention in UIA patients to evaluate unstable states showed a positive predictive value of 100% and an accuracy of 78.3%, indicating excellent distinguishability.

[0061] This invention also provides an application of the biomarker combination used to construct an intracranial aneurysm stability assessment model in a detection reagent. The detection reagent is composed of reagents that detect the plasma expression levels of the biomarker combination factors PYGL, GSTP1, and STC2. The reagent includes: three pre-coated ELISA plates, respectively coated with human monoclonal anti-PYGL antibody, human monoclonal anti-GSTP1 antibody, and human monoclonal anti-STC2 antibody; horseradish peroxidase-labeled rabbit / sheep anti-PYGL detection antibody, anti-GSTP1 detection antibody, and anti-STC2 detection antibody; and three sets of gradient standards, namely, human recombinant PYGL protein standard, human recombinant GSTP1 protein standard, and human recombinant STC2 protein standard.

[0062] This invention also provides an application of the aforementioned biomarker combination in constructing an intracranial aneurysm stability assessment model within a kit. The kit's detection reagents consist of reagents for detecting the plasma expression levels of the biomarker combination factors PYGL, GSTP1, and STC2. These detection reagents are key components of the kit for realizing the intracranial aneurysm stability assessment model and can be used with the aforementioned assessment 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 PYGL, GSTP1, and STC2, to establish a comprehensive assessment model for early warning of intracranial aneurysm instability, reducing treatment risks and patient suffering. Four methods for assessing aneurysm instability were used in a selected population of intracranial aneurysm patients: the PHASES score, the ELPASS score, the unruptured intracranial aneurysm treatment score, and the assessment 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 assessment model's accuracy rate was 78.3%. This indicates that the model is superior to 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 an intracranial aneurysm stability assessment model, characterized in that: The biomarker combination consists of factors: PYGL, GSTP1, and STC2. A predictive model for evaluating the stability of intracranial aneurysms is constructed based on these biomarkers.

2. The application in the evaluation model according to claim 1, characterized in that: The evaluation model is expressed by the following formula: ln p / (1-p) = 0.288*C PYGL + 0.253*C GSTP1 + 0.082*C STC2 + (-16.081) C PYGL C GSTP1 and C STC2 The abundance of PYGL, GSTP1, and STC2 in the sample plasma are respectively assigned specific measured values, and the probability p of the aneurysmal instability state risk of the sample is calculated.

3. The application of a biomarker combination-based intracranial aneurysm stability assessment model in a detection reagent, characterized in that: The detection reagent consists of reagents for detecting the plasma expression levels of the biomarker combination factors PYGL, GSTP1, and STC2.

4. The application of a biomarker combination-based intracranial aneurysm stability assessment model 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 PYGL, GSTP1, and STC2.

5. A method for applying the evaluation model according to claim 1 or 2, characterized in that: An assessment model for evaluating the stability of intracranial aneurysms was constructed based on the combination of biomarkers, and a nomogram was established. The probability of unstable state risk was evaluated by scoring the nomogram. We set up the plasma PYGL abundance range, plasma GSTP1 abundance range, plasma STC2 abundance norm axis, risk score axis, total risk score axis, and unstable state risk probability axis respectively. We took the risk score value for each plasma abundance value of PYGL, GSTP1, and STC2 on the risk score axis, calculated the sum of each risk score value, found the corresponding total risk score value on the total risk score axis, and finally found the corresponding risk probability on the unstable state risk probability axis.

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