Biomarker for predicting in-stent restenosis after superior mesenteric artery stent implantation

Abstract

The invention discloses a biomarker for predicting in-stent restenosis after a superior mesenteric artery stent implantation operation. The biomarker is one of isopropyl sulfide and 1-methyl-L-histidine or a combination of isopropyl sulfide and 1-methyl-L-histidine. According to the method, a new strategy is provided for preoperative prediction and early intervention of ISR after SISMAD operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical technology, in particular to a biomarker for predicting in-stent restenosis after superior mesenteric artery stent implantation. BACKGROUND

[0002] Superior Mesenteric Artery Dissection (SMAD) is a rare but severe vascular disease characterized by the tearing of the inner layer of the superior mesenteric artery wall, leading to the formation of a dissection between the intima and media. SMAD can cause insufficient blood supply to the intestines, ischemic enteritis, and even intestinal necrosis, posing a serious threat to patients' lives. Stent implantation, as a minimally invasive vascular reconstruction technique, has become one of the main methods for treating SMAD, especially when conservative treatment fails or intestinal ischemia worsens, it is beneficial to improve symptoms and reshape the normal configuration of the artery. However, in-stent restenosis (ISR) after stent implantation is a common complication, and its incidence significantly affects the long-term prognosis of patients. ISR is usually caused by intimal hyperplasia, vascular remodeling, and inflammatory response, and these processes involve complex molecular mechanisms. However, existing imaging methods (such as ultrasound, CT angiography, etc.) can only detect the restenosis lesion after it forms, lacking the ability to predict early. This delayed diagnostic approach results in some patients being discovered when the lesion has progressed to obvious clinical symptoms, thus missing the best opportunity for early intervention. Therefore, there is an urgent need for a sensitive and non-invasive prediction method to identify high-risk patients early and guide postoperative individualized management strategies.

[0003] Metabolomics is a key technology for studying the changes of small molecule metabolites in the body, and is mainly used to reveal the metabolic characteristics and potential pathological mechanisms of diseases. In particular, non-targeted metabolomics can comprehensively capture the change patterns of metabolites through high-throughput analysis, providing new ideas for the molecular mechanism research and biomarker discovery of complex diseases. In recent years, non-targeted metabolomics has been successfully applied to the study of various cardiovascular diseases (such as coronary heart disease, aortic dissection, etc.) and metabolic diseases, revealing the dynamic changes of metabolic networks during disease progression. In stent-related diseases, metabolomics research has also shown initial results. For example, some studies have found that certain amino acids and lipid metabolites in the plasma are closely related to in-stent restenosis in coronary artery, providing scientific basis for exploring non-invasive prediction methods. SUMMARY

[0004] The purpose of the present application is to provide a biomarker for predicting in-stent restenosis after superior mesenteric artery stent implantation, providing a new strategy for preoperative prediction and early intervention of post-SISMAD ISR.

[0005] The technical scheme adopted by the present application to solve its technical problems is: A biomarker for predicting in-stent restenosis after supermesenteric artery stent implantation, the biomarker being one or a combination of isopropyl sulfide and 1-methyl-L-histidine.

[0006] The cut-off value of isopropyl sulfide is 90011141.48; the cut-off value of 1-methyl-L-histidine is 795313.51; if one of isopropyl sulfide and 1-methyl-L-histidine is abnormal, it is determined to be abnormal, and if both are abnormal, it is more accurate. When the content of the biomarker is less than the cut-off value, it represents abnormality. The calculation method of the cut-off value is described in the previous patent 2025104303938 of the applicant.

[0007] Application of isopropyl sulfide as a biomarker for predicting in-stent restenosis after supermesenteric artery stent implantation.

[0008] Application of 1-methyl-L-histidine as a biomarker for predicting in-stent restenosis after supermesenteric artery stent implantation.

[0009] The present application has the advantages that: the present application provides a new strategy for preoperative prediction and early intervention of postoperative ISR of SISMAD; the present application can early identify high-risk patients, and after finding abnormalities, measures can be taken: 1) prolong the use time of postoperative anti-platelet drugs; 2) shorten the postoperative review time. If vascular restenosis is found, timely surgical intervention can be performed. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 is a metabolic profile detection result graph of SR and SP; A. The method of PCA is used to observe the overall distribution trend between the two groups of samples B. PLS-DA analysis uses partial least squares regression to establish a relationship model between metabolite expression and sample category, to realize the prediction of sample category. The PLS-DA model of each comparison group is established, and the model evaluation parameters (R2, Q2) obtained by 7-fold cross-validation (7-cycle cross-validation, when the number of biological repeats of samples n<=3, k-cycle cross-validation, k=2n) are obtained. If R2 and Q2 are closer to 1, the model is more stable and reliable. C. In order to judge the quality of the model, the model will also be sorted and verified to test whether the model is "over-fitted".

[0011] Figure 2 is a volcano plot of differential metabolites between SR and SP groups.

[0012] Figure 3 is a matchstick plot of differential metabolites between SR and SP groups.

[0013] Figure 4 Figure 1 is a diagnostic value analysis chart of SR and SP group intermetabolite differences. DETAILED DESCRIPTION

[0014] The technical solutions of the present application are further specifically described below through specific examples.

[0015] In the present application, unless specified, the raw materials and equipment used can be purchased from the market or commonly used in the art. The methods in the following examples are all conventional methods in the art, unless otherwise specified.

[0016] Example 1: I. Sample collection and processing 1. Research subjects: 30 patients diagnosed with SISMAD and undergoing stent implantation were selected from the First Affiliated Hospital of Zhejiang University School of Medicine (ethical approval number: IIT20240766B-R1), including 23 cases of stent patency (SP) and 7 cases of stent restenosis (SR).

[0017] 2. Sample processing: 10 ml of peripheral venous blood of the research subjects was collected and placed in an EDTA anticoagulant tube. Centrifugation was performed at room temperature at 3000 rpm for 10 min (Thermo ST 16R, USA). After centrifugation, the upper light yellow plasma was taken and aliquoted into 1 ml cryogenic tubes, with a plasma volume of ≥200 ml in each tube. Then, it was quickly frozen in liquid nitrogen for 30 s, transported to the biological sample library, and stored in a -80°C low-temperature refrigerator to avoid repeated freezing and thawing, and waiting for sequencing on the machine.

[0018] II. Non-targeted metabolomics analysis 1. Experimental method: Plasma metabolite extraction 1) Use a pipette to draw 100 μL of plasma sample into a 1.5 ml sterile EP tube, and add 400 μL of extraction solution (methanol: acetonitrile = 1:1); 2) vortex for 30 s until it is mixed well, and ultrasonic in an ice water bath for 10 min; 3) Place all samples at -40°C for 60 min; 4) centrifuge at 12000 rpm at 4°C for 15 min, and draw 400 μL of supernatant into a new 1.5 ml sterile EP tube, and vacuum dry for 15 min; 5) add 200 μL of 50% acetonitrile for reconstitution, vortex for 30 s, and ultrasonic in an ice water bath for 10 min; 6) centrifuge all samples at 13000 rpm at 4°C for 15 min; 7) Take 75 μL of supernatant into a sample bottle for machine detection; 8) Mix 10 μL of supernatant from all samples into a quality control sample for machine detection.

[0019] 2. Machine detection The present application uses an ultra-performance liquid chromatography (UPLC, Agilent 1290) to perform chromatographic separation of the target compound through a liquid chromatography column (2.1x100 mm, 1.7 μm). The liquid chromatography A phase is an aqueous phase, i.e. a stationary phase, containing 25 mmol / L of ammonium acetate and 25 mmol / L of ammonia water, and the B phase is an organic phase containing acetonitrile, i.e. a mobile phase. Gradient elution method is used: 0~0.5 min, 95% B; 0.5~7 min, 95%~65% B; 7~8 min, 65%~40% B; 8~9 min, 40% B; 9~9.1 min, 40%~95% B; 9.1~12 min, 95% B. The flow rate of the mobile phase is 0.5 ml / min, the column temperature is 25 ℃, the sample disc temperature is 4 ℃, the injection volume is 2 μL in positive mode (POS) and 2 μL in negative mode (NEG).

[0020] The present application uses a high-resolution triple quadrupole time-of-flight mass spectrometer (Triple Q-TOF-MS) 6600 to perform high-resolution mass spectrometry data acquisition through an information-dependent scanning (IDA) mode. In IDA mode, the data acquisition software (Analyst TF version 1.7, AB Sciex) automatically selects ions and collects their secondary mass spectrometry data according to the primary mass spectrometry data and the pre-set standards. 12 ions with the highest intensity and >100 are selected for secondary mass spectrometry scanning in each cycle, the collision-induced dissociation energy is 30 eV, and the cycle time is 0.56 s. The ion source parameters are as follows: ① atomization gas pressure 60 psi; ② auxiliary gas pressure 60 psi; ③ curtain gas pressure 35 psi; ④ ion source temperature 600 ℃; ⑤ de-clustering voltage 60 V; ⑥ POS and NEG mode electrospray voltage is 5000 V or -4000 V, respectively.

[0021] 3. Data processing The raw data of mass spectrometry was converted into mzXML format using ProteoWizard software version 3.0 (http: / / proteowizard.sourceforge.net / ). The data baseline filtering and calibration, raw peak extraction, peak identification, peak alignment, peak integration and other steps were completed using the mass spectrometry data analysis software of endogenous metabolites XCMS version 3.2, and the minfrac standard was set to 0.5 and the critical value was set to 0.3. At the same time, the R language package and secondary mass spectrometry database including human metabolome database (human metabolome database, HMDB), mass bank of North America (mass bank of North America, MoNA) and METLIN were used for peak (Peak) substance identification.

[0022] 4. Data analysis 4.1 Raw data preprocessing 1) Filter single Peak to remove noise, filter based on relative standard deviation of deviation value; 2) Filter single Peak, only keep the peak area data of AAA group and / or HC group null value ≤ 50%; 3) Simulate missing values, fill in half of the smallest value in the existing data; 4) According to the QC sample, simulate the signal change in the data acquisition process, and normalize the data.

[0023] 4.2 PCA analysis The data set obtained by metabolomics detection is diversified, and the diversified data set needs high-dimensional spatial coordinate system to be displayed, so it is necessary to convert high-dimensional data to low-dimensional by PCA. Using SIMCA software version 15.0.2, first log transformation and centering of the original data, then format processing, and finally automatic modeling analysis.

[0024] 4.3 PLS-DA analysis First, the first principal component was modeled by partial least squares-discrimination analysis (PLS-DA), and the model quality test used 7-fold cross-validation. The evaluation index of the validity of the model after verification mainly includes: ① R2Y value-the explainability of the model to the classification variable Y; ② Q value-the predictability of the model; On the basis of the above evaluation, permutation test was used to randomly change the order of the classification variable Y to obtain the corresponding random Q value, and the validity of the model was tested again.

[0025] 4.4 Multivariate statistical analysis The present application uses multivariate statistics to analyze data. Past univariate statistics such as t-test, analysis of variance, etc. only focus on the individual changes of metabolite expression. In contrast, multivariate statistics is more in line with the characteristics of non-targeted metabolomics data sets, and its advantages can be summarized as two levels, one level is to find the potential relationship between metabolites, and the other level is to explore the possible promotion and antagonism of different metabolites in the pathophysiological process of the body. Using two types of statistical methods at the same time is not only beneficial to consider data from different angles, but also can greatly reduce the possibility of overfitting or false positive results. The indicators and thresholds used in the preliminary screening of metabolites in the present application are P < 0.05 of t-test, and variable importance in the projection (VIP) > 1 of the first principal component of the OPLS-DA model, and the added indicator in the secondary screening is the absolute value of the logarithmic transformation (with base 2) of the fold change of the expression difference of a metabolite between the AAA group and the HC group, that is, |log2(fold change)| > 1.

[0026] 4.5 Hierarchical clustering analysis of differential metabolites The present application calculates the Euclidean distance matrix of the quantitative values of differential metabolites, and performs hierarchical clustering of differential metabolites in a completely linked manner. From the perspective of biological structure and function, the differential metabolites obtained by the foregoing analysis have certain similarity and / or complementarity, or are regulated by the same metabolic pathway, and the expression characteristics between the AAA group and the HC group are similar or opposite. By integrating this type of characteristics through hierarchical clustering analysis, metabolites with similar characteristics can be classified into a class, which is beneficial to explore the characteristics of the changes of plasma metabolites between the AAA group and the HC group.

[0027] 4.6 Correlation analysis of differential metabolites The present application uses Pearson test to calculate the correlation of the quantitative values of differential metabolites. The correlation level between two metabolites is measured by the value of the correlation coefficient R. When positively correlated, 0 < R < 1; when negatively correlated, -1 < R < 0. The closer the absolute value of R is to 1, the stronger the correlation between the two variables, and the closer the absolute value of R is to 0, the weaker the correlation between the two variables.

[0028] 4.7 Receiver operating characteristic curve (ROC) analysis of differential metabolites The receiver operating characteristic curve (ROC) analysis of differential metabolites is performed and the area under the curve (AUC) is calculated.

[0029] 5. Result analysis Figure 1 are the results of metabolic profiling of patients in stent restenosis (SR) and stent patency (SP) groups: Figure 1 A, B, C show that there are significant differences in metabolic profiling between SR and SP groups (overall), and the model is well constructed, and the data is suitable for further difference analysis.

[0030] Figure 2 are the intuitive display of differential metabolites between SR and SP groups. The volcano plot can intuitively display the overall distribution of differential metabolites. The horizontal coordinate represents the fold change (log2(Fold Change)) of metabolites in different groups, and the vertical coordinate represents the significance level (-log10(P-value)). Each point in the volcano plot represents a metabolite. Red points represent significantly up-regulated metabolites, and blue points represent significantly down-regulated metabolites. The size of the circle represents the VIP. Figure 2 The results show that there are significant differences in plasma metabolic profiling between SR and SP groups, suggesting further screening of valuable differential metabolites for diagnosis and prediction.

[0031] Table 1 is the screening result of differential metabolites between SR and SP groups. The screening of differential metabolites mainly refers to three parameters: VIP, FC and P value. VIP refers to the variable importance in the projection of the first principal component of the PLS-DA model, and the VIP value represents the contribution of the metabolite to the grouping. FC refers to the fold change (Fold Change), which is the ratio of the mean of all biological replicate quantitative values of each metabolite in the comparison group. P value is calculated by T-test, which represents the significance level of the difference. The threshold is set as VIP>1.0, FC>1.5 or FC<0.667 and P value<0.05. The screened differential metabolites are shown in the following table: Table 1 .

[0032] Figure 3 are the matchstick plot displays of differential metabolites between SR and SP groups. According to the differential metabolites obtained by comparing each group combination, the matchstick plot is drawn, which can clearly represent the up-regulated and down-regulated metabolites and the metabolites with large fold change. We take the top 20 up-regulated and down-regulated metabolites by taking the log2(Fold Change) value of the differential metabolites as the base 2 and sorting them, and then display the matchstick plot. The color of the point represents the up-regulation and down-regulation, blue represents down-regulation, and red represents up-regulation. The length of the rod represents the size of log2(Fold Change). The size of the point represents the size of the VIP value.

[0033] Figure 4 The diagnostic value of SR and SP group intermetabolites. ROC analysis was performed using MetaboAnalyst 6.0, and the maximum AUC value of a single metabolite was 0.913, with good diagnostic accuracy. Figure 4 A shows the content comparison of the metabolite with the largest AUC value after ROC analysis, i.e. Diisopropyl sulfide (isopropyl sulfide), in the plasma of SR and SR patients. Isopropyl sulfide was significantly reduced in the plasma of SR patients (P<0.001); 4B shows the content comparison of the second largest metabolite after ROC analysis, i.e. 1-Methylhistidine (1-methyl-L-histidine), in the plasma of SR and SR patients. 1-methyl-L-histidine was significantly reduced in the plasma of SR patients (P<0.01); Figure 4 C shows the ROC curve analysis of isopropyl sulfide (AUC value = 0.913); Figure 4 D shows the ROC curve analysis of 1-methyl-L-histidine (AUC value = 0.901). The AUC value is close to 1, and the diagnostic value is significant.

[0034] The cut-off value of isopropyl sulfide is 90011141.48; the cut-off value of 1-methyl-L-histidine is 795313.51; if one of isopropyl sulfide and 1-methyl-L-histidine is abnormal, it is determined to be abnormal, and the probability of SISMAD stent restenosis after implantation is increased, and both are abnormal, which is more accurate. When the content of the biomarker is less than the cut-off value, it represents abnormality.

[0035] The above-described embodiments are only a preferred scheme of the present application, and do not limit the present application in any form. Other variants and modifications can be made without exceeding the technical solutions recited in the claims.

Claims

1. A biomarker for predicting in-stent restenosis after superior mesenteric artery stent implantation, characterized by, The biomarker is one or a combination of isopropyl sulfide, 1-methyl-L-histidine.

2. Use of isopropyl sulfide as a biomarker for predicting in-stent restenosis after a superior mesenteric artery stent implantation.

3. Use of 1-methyl-L-histidine as a biomarker for predicting in-stent restenosis after a superior mesenteric artery stent implantation.

Images