Biomarker method for predicting curative effect of cyclosporine on aplastic anemia

Abstract

The invention relates to the technical field of aplastic anemia (AA) treatment research, discloses a biomarker method for predicting the curative effect of cyclosporine on aplastic anemia, and aims to detect plasma differential metabolites of patients with aplastic anemia by adopting a metabonomics technology, find potential biomarkers for treating AA patients and find the curative effect of cyclosporine on the AA patients. CsA) is used for treating the in-vivo metabolism of AA. The method is based on liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) technologies, and comprises the following steps: collecting 5 plasma samples of AA patients, 5 plasma samples of healthy people and 5 plasma samples of patients in a stable period after treatment for comparative analysis; and screening differential compounds by adopting a multivariate statistical method, and further performing enrichment analysis on differential metabolites. Research of the method shows that in-vivo metabolites of AA patients and healthy people are obviously different, and the way related to immunity and energy metabolism may be a key target for treating AA; csA intervenes the occurrence and development of AA by regulating and controlling immune-related metabolic ways.

Description

Technical Field

[0001] The present invention relates to the technical field of aplastic anemia treatment research, and in particular to a biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia. Background Art

[0002] Aplastic Anemia (AA) is a hematopoietic dysfunction caused by multiple factors, which leads to a decrease in the total amount of red bone marrow and the number of hematopoietic cells, followed by bone marrow fatty degeneration and hematopoietic function exhaustion. The main clinical manifestations are anemia, bleeding and severe infection. According to epidemiological statistical studies, aplastic anemia in my country mainly occurs in older children and the elderly, with a bimodal distribution. The course of most cases shows a trend of chronic deterioration, and the median survival time is about 174 months. In the past, it was believed that under a certain genetic background, the pathogenesis of aplastic anemia was the result of the combined effects of hematopoietic stem cell / progenitor cell defects, abnormal bone marrow microenvironment, and immune dysfunction. Under this understanding, immunosuppressive therapy (IST) and hematopoietic stem cell transplantation (HSCT) are the main methods for treating aplastic anemia. Due to many unfavorable factors such as donor source, age and complications, the proportion of IST is significantly higher than that of HSCT.

[0003] With the deepening of research on the pathogenesis of AA, the immune damage of bone marrow hematopoietic stem cells caused by T cell dysfunction has been widely recognized. More and more scholars believe that aplastic anemia is an autoimmune disease. Currently, immunosuppressants used for immune abnormalities, such as antilymphocyte globulin (ALG), antithymocyte globulin (ATG), cyclosporine A (CsA), etc., have a good therapeutic effect on aplastic anemia. From the treatment level, it has also been confirmed that this disease is related to immune function damage. As a classic first-line drug, CsA can significantly reduce the proportion of bone marrow lymphocytes in patients with aplastic anemia and partially reverse bone marrow failure. The traditional view is that cyclosporine can combine with cyclophilin to form a complex, inhibit the activity of calcineurin, and ultimately downregulate the expression of inflammatory factors such as IL-2, IFN-γ, TNF-α, and inhibit T cell proliferation, thereby achieving the purpose of treating aplastic anemia. It is relatively cheap and widely used, but it is not very clear what effect it has on the endogenous metabolism of patients with aplastic anemia.

[0004] Therefore, it is necessary to provide a biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia. The method uses the metabolomics technology combined with UPLC-MS and GC-MS to conduct metabolomics research on the plasma of patients with aplastic anemia before treatment, after CsA treatment and healthy controls, so as to seek different characteristic metabolites of patients with aplastic anemia before and after treatment and healthy people, provide new ideas for early diagnosis of the disease, provide an experimental basis for exploring the pathogenesis of AA, and provide possibilities for targeted treatment of the disease.

[0006] The method for predicting the biomarker efficacy of cyclosporine in treating aplastic anemia provided by the present invention comprises the following steps:

[0007] S1. Select healthy people and newly diagnosed patients with aplastic anemia and patients in stable stage of the disease, and establish a healthy control group (blank group), a model group (AA group), and a treatment group to form a research model;

[0008] S2. Collect biological samples from healthy people, newly diagnosed patients with aplastic anemia, and patients in the stable stage of the disease, wherein the biological samples are blood samples;

[0009] S3. Perform metabolomics studies and analyses of biological samples, including liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) analysis, to identify and quantify metabolites;

[0010] S4. Use multivariate statistical methods to screen out metabolites that are differentially expressed in patients with aplastic anemia compared with healthy controls;

[0011] S5. Identify differential metabolites associated with the therapeutic efficacy of cyclosporine (CsA);

[0012] S6. Based on the expression levels of the above-mentioned differential metabolites, predict the efficacy of cyclosporine in the treatment of aplastic anemia.

[0013] Preferably, the inclusion and exclusion criteria for the research model selection are:

[0014] The following criteria were used when selecting medical records: ① patients diagnosed with AA for the first time and without other blood diseases; ② no history of endocrine system diseases; plasma samples were obtained from the secondary use of patients' blood routine samples;

[0015] Exclusion criteria: ① patients with severe heart, lung, kidney and other important organ dysfunction; ② patients who received blood transfusion during treatment; ③ patients with malignant tumors; ④ patients with mental and intellectual disabilities; ⑤ patients who were breastfeeding or pregnant.

[0016] Preferably, performing metabolomics research on the biological sample comprises the steps of:

[0017] P1. Sample processing, including liquid sample analysis and gas phase sample processing;

[0018] P2. Chromatographic and mass spectrometric conditions:

[0019] P2.1, LC-MS conditions:

[0020] Chromatographic column: ACQUITYUPLC HSS T3 (100 mm × 2.1 mm, 1.8 um); mobile phase: phase A is water (containing 0.1% formic acid), phase B is acetonitrile; flow rate: 0.35 mL / min; injection volume: 3 μL; column temperature: 35 ° C; gradient elution program: 0-2.0 min 5% B, 2.0-4.0 min 5-35% B, 4.0-8.0 min 35%-55% B, 8.0-10.0 min 55-75% B, 10.0-14.0 min 75-95% B, 14.0-15.0 min 95% B; mass spectrometry conditions: positive ion and negative mode, primary scan range of m / z: 100-1500, scanning collision mode of high energy collision dissociation (HCD), heater temperature of 320°C, electrospray voltage of 3.5KV, capillary temperature of 350°C;

[0021] P2.2, GC-MS conditions:

[0022] The DB-5MS capillary column (30m×0.25mm×0.25μm) was used as carrier gas for high-purity helium; program temperature rise: initial temperature was 60℃, maintained for 0.5min; program temperature rise to 125℃ at 8℃ / min; temperature rise to 210℃ at 8℃ / min; temperature rise to 270℃ at 15℃ / min; temperature rise to 305℃ at 20℃ / min, maintained for 5min. Flow rate 1.0mL / min, injection port temperature: 260℃. Injection volume: 2μL. Ion source: electron impact source (EI), source temperature: 230℃, electron energy: 70eV, quadrupole temperature: 150℃. Scanning mode: full scan mode (SCAN), mass scanning range: m / z 50-500;

[0023] P3. Data processing:

[0024] Metabolomics processing software Progenesis QI and MS-DIAL were used to perform baseline filtering, retention time correction, chromatographic peak identification, alignment and normalization on the raw data. Substance identification was performed by retention time, ion fragmentation, combined with Thermo mzCloud online database, Thermo mzValut local database, ChemSpider database, etc. The normalized peak areas were subjected to principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal-partial least squares discriminant analysis (OPLS-DA). VIP (variable importance in the projection)>1.0 and P<0.05 were used as conditions to screen the differential metabolites between the AA group and the blank group, and between the AA group and the treatment group, and observe the intervention of CsA. MetaboAnalyst5.0 (http: / / www.metaboanalyst.ca / ) was used to analyze the differential metabolite data and perform metabolic pathway enrichment analysis.

[0025] Preferably, the liquid sample analysis comprises the steps of: taking out a plasma sample stored at -80°C, thawing it in a 4°C environment, transferring 200 μL of plasma into a 1.5 mL EP tube; adding 800 μL of methanol-acetonitrile (V:V=2:1) ​​for protein precipitation, vortexing for 1 min; centrifuging for 10 min (12000 rpm, 4°C), transferring a portion of the supernatant to a liquid injection vial for LC-MS analysis, and placing another portion of the supernatant into a glass derivatization vial for GC-MS detection;

[0026] The gas phase sample processing comprises the following steps: taking a glass derivatization vial containing a supernatant, and using a centrifugal concentrator to evaporate the sample; adding 80 μL of a 15 mg / mL trimethoprim-pyridine hydrochloride solution to the vial, and performing an oximation reaction in a 37° C. shaking incubator for 60 minutes; then adding 50 μL of a BSTFA derivatization reagent and 20 μL of n-hexane, and reacting at 70° C. for 60 minutes; and performing GC-MS detection after standing at room temperature.

[0027] Preferably, performing metabolomics analysis on the biological sample comprises the steps of:

[0028] Q1. Data quality evaluation; Q2. Multivariate statistical analysis; Q3. Screening of differential metabolites and pathway enrichment analysis, including comparative analysis between the AA group and the healthy control group, and between the AA group and the treatment group.

[0029] Preferably, the differential metabolites include but are not limited to uracil, PS (21:0 / 22:6), PC (38:6), 1-Octadecene, cytidine monophosphate and inosinic acid, cytidine monophosphate and inosinic acid.

[0030] Compared with the related art, the biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia provided by the present invention has the following beneficial effects:

[0031] The present invention can provide personalized treatment plans for AA patients and improve the treatment effect by identifying specific metabolites associated with CsA treatment response;

[0032] By monitoring changes in specific metabolites, the present invention helps to diagnose AA at an early stage, thus buying more treatment time for patients;

[0033] The present invention can monitor the metabolic changes during CsA treatment in real time, timely evaluate the treatment effect, and provide guidance for clinical treatment;

[0034] By predicting the efficacy, we can reduce ineffective treatments, lower drug side effects, and improve patients’ quality of life;

[0035] The present invention provides a scientific basis for the treatment of AA with CsA, helps to deeply understand its mechanism of action, and provides a reference for the development of new treatment methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is the QC sample data quality evaluation chart:

[0037] A: PCA score graph in LC-MS mode; B: PCA score graph in GC-MS mode; C: Metabolite intensity box plot; D: Correlation graph between samples;

[0038] Figure 2 This is the multivariate statistical analysis chart of plasma sample data:

[0039] A: PLS-DA scoring graph in LC-MS mode; B: PLS-DA scoring graph in GC-MS mode; C: Splot scoring graph in LC-MS mode; D: Splot scoring graph in GC-MS mode;

[0040] Figure 3 This is the OPLS-DA analysis diagram of plasma sample data:

[0041] A: OPLS-DA score diagram of LC-MS of blank group and model group; B: OPLS-DA score diagram of GC-MS of blank group and model group; C: OPLS-DA score diagram of LC-MS of treatment group and model group; D: OPLS-DA score diagram of GC-MS of treatment group and model group; E: RPT score diagram in LC-MS mode; F: RPT score diagram in GC-MS mode;

[0042] Figure 4 Screening and pathway enrichment analysis of differential metabolites between the healthy control group and the AA group:

[0043] A: Volcano map of differential metabolites; B: Heat map of differential metabolites; C: Lolipopmap of differential metabolites; D: Correlation map of differential metabolites;

[0044] Figure 5 Screening and pathway enrichment analysis of differential metabolites between the treatment group and the AA group:

[0045] A: Volcano map of differential metabolites; B: Heat map of differential metabolites; C: Lolipopmap of differential metabolites; D: Correlation map of differential metabolites. DETAILED DESCRIPTION

[0046] The biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia proposed by the present invention is further described below in conjunction with the accompanying drawings and implementation examples.

[0047] 1.1 Study population and groups

[0048] Patients with aplastic anemia were selected, including 5 newly diagnosed patients (model group, M), including 2 males and 3 females, with a median age of 43 (31-70) years old; 5 patients in the stable stage of the disease (treatment group, T), including 1 male and 4 females, with a median age of 46 (31-69) years old. There were 5 healthy controls (blank group, B), including 2 males and 3 females, with a median age of 40 (24-60) years old.

[0049] Inclusion criteria: The following criteria were used when selecting medical records: ① patients with the first diagnosis of AA and without other blood diseases; ② no history of endocrine system diseases; plasma samples were obtained from the secondary use of patients' routine blood samples. This study has been approved by the Ethics Committee of this hospital (Ethics Review 2023 Department 101). All selected patients were informed of the secondary use of blood and signed informed consent.

[0050] Exclusion criteria: ① patients with severe heart, lung, kidney and other important organ dysfunction; ② patients who received blood transfusion during treatment; ③ patients with malignant tumors; ④ patients with mental and intellectual disabilities; ⑤ patients who were breastfeeding or pregnant.

[0051] 1.2 Materials

[0052] The instruments used in the present invention include: ACQUITY UPLC I-Class plus ultra-high performance liquid chromatography; QExactive HFX high-resolution mass spectrometer, 8890-5977B gas chromatography-mass spectrometer; and N9500 ​​fully automatic blood analyzer.

[0053] The reagents used in the present invention: methanol, acetonitrile, chloroform, n-hexane, pyridine and formic acid are of mass spectrometry grade, and water is homemade ultrapure water.

[0054] 1.3 Metabolomics research

[0055] 1.3.1 Sample processing

[0056] Liquid sample analysis: The plasma samples stored at -80°C were taken out and thawed at 4°C. 200 μL of plasma was transferred to a 1.5 mL EP tube. 800 μL of methanol-acetonitrile (V:V=2:1) ​​was added for protein precipitation. The samples were vortexed for 1 min. The samples were centrifuged for 10 min (12000 rpm, 4°C). A portion of the supernatant was transferred to a liquid injection vial for LC-MS analysis. The other portion of the supernatant was placed in a glass derivatization vial for GC-MS detection.

[0057] Gas phase sample treatment: Take the glass derivatization vial containing the supernatant and evaporate the sample using a centrifugal concentrator dryer; add 80 μL of 15 mg / mL trimethoprim-pyridine hydrochloride solution to the vial, and perform oximation reaction in a 37°C shaking incubator for 60 min. Continue to add 50 μL BSTFA derivatization reagent and 20 μL n-hexane, react at 70°C for 60 min, and perform GC-MS detection after standing at room temperature.

[0058] It should be noted that the quality control sample (QC) was prepared by mixing all sample solutions in equal volumes.

[0059] 1.3.2 Chromatographic and mass spectrometric conditions

[0060] LC-MS conditions: chromatographic column: ACQUITYUPLC HSS T3 (100 mm×2.1 mm, 1.8 um); mobile phase: phase A is water (containing 0.1% formic acid), phase B is acetonitrile; flow rate: 0.35 mL / min; injection volume: 3 μL; column temperature: 35°C; gradient elution program: 0-2.0 min 5% B; 2.0-4.0 min 5-35% B; 4.0-8.0 min 35%-55% B; 8.0-10.0 min 55-75% B; 10.0-14.0 min 75-95% B; 14.0-15.0 min 95% B. Mass spectrometry conditions: positive ion and negative mode: primary scan range: m / z: 100-1500; scanning collision mode: high energy collision dissociation (HCD); heater temperature: 320°C; electrospray voltage: 3.5KV; capillary temperature: 350°C.

[0061] GC-MS conditions: DB-5MS capillary column (30m×0.25mm×0.25μm), carrier gas is high-purity helium. Program temperature rise: initial temperature is 60℃, hold for 0.5min; program temperature rise to 125℃ at 8℃ / min; temperature rise to 210℃ at 8℃ / min; temperature rise to 270℃ at 15℃ / min; temperature rise to 305℃ at 20℃ / min, hold for 5min. Flow rate 1.0mL / min, injection port temperature: 260℃. Injection volume: 2μL. Ion source: electron impact source (EI), source temperature: 230℃, electron energy: 70eV, quadrupole temperature: 150℃. Scan mode: full scan mode (SCAN), mass scan range: m / z 50-500.

[0062] 1.3.3 Data processing

[0063] Metabolomics processing software Progenesis QI and MS-DIAL were used to perform baseline filtering, retention time correction, chromatographic peak identification, alignment and normalization on the raw data. Substance identification was performed by retention time, ion fragmentation, combined with Thermo mzCloud online database, Thermo mzValut local database, ChemSpider database, etc. The normalized peak areas were subjected to principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal-partial least squares discriminant analysis (OPLS-DA). VIP (variable importance in the projection)>1.0 and P<0.05 were used as conditions to screen the differential metabolites between the AA group and the blank group, and between the AA group and the treatment group, and observe the intervention of CsA. MetaboAnalyst 5.0 (http: / / www.metaboanalyst.ca / ) was used to analyze the differential metabolite data and perform metabolic pathway enrichment analysis.

[0064] 1.4 Statistical analysis

[0065] SPSS 17.0 software was used for statistical analysis. Experimental data were expressed as x±s, and LSD-t test was used for comparison among multiple groups.

[0066] 2 Results

[0067] 2.1 Hematological indicators of the study population

[0068] In clinical practice, the main symptoms of patients with newly diagnosed aplastic anemia are weakness, fatigue, palpitations, and pale complexion. If there is bleeding, it is relatively mild, and internal bleeding is not common. Infections and fever are generally mild and late, and are easier to control after treatment. Many patients with newly diagnosed aplastic anemia have obvious fluctuations in blood counts in the early stages of the disease. It is common for a decrease in one line to turn into a decrease in two or three lines. There are also cases where there is a decrease in all blood cells from the beginning. CsA is currently a blockbuster drug for the clinical treatment of aplastic anemia. It can relieve symptoms, but it cannot solve the problem of hematopoietic failure. Similarly, due to the side effects of the drug, patients are often unable to accept long-term treatment, and treatment is often interrupted for various reasons. After stopping the drug, the disease relapses again, and the disease cannot be effectively controlled. As shown in Table 1, the red blood cells, platelets and hemoglobin of the five patients with newly diagnosed aplastic anemia selected in this study were significantly different from those of the healthy population (P<0.01), but there was no significant difference in white blood cells (P>0.05); the platelets of the stable patients after CsA treatment were significantly different from those of the model group (P<0.01), and there were no significant differences in other indicators (P>0.05).

[0069] Table 1 Hematological parameters of the study population

[0070]

[0071] Compared with the blank group, ** P<0.01; compared with the model group, ## P<0.01.

[0072] 2.2 Plasma metabolomics analysis

[0073] 2.2.1 Data quality evaluation

[0074] The data of QC samples are analyzed to evaluate their quality and reliability. Figure 1 As shown in A-1B, the PCA results show that the QC samples (green dots) are closely clustered together, indicating that the experiment is stable and has good reproducibility. Figure 1 C. The height of the box reflects the degree of data fluctuation to a certain extent. The flatter the box and the shorter the whiskers, the more concentrated the data. Figure 1 D shows that the correlation between QC samples is also very high. In summary, the chromatography-mass spectrometry system operates stably and the data are reliable.

[0075] 2.2.2 Multivariate statistical analysis

[0076] Multivariate statistical analysis was performed on the plasma sample data. Unsupervised PCA was first used to study the overall distribution of samples. The results are shown in Figure 1A-1B, when the coordinate point of AA patient (orange point) is relatively far away from the healthy control group (blue point) on the score graph, there is a certain degree of separation, which means that the difference between the samples is significant. PLS-DA is a supervised discriminant statistical method that better explains and predicts the difference between the two groups of samples. Figure 2 As shown in A-2B, the healthy control group is significantly separated from the AA group and the treatment group (green dots) on the score graph. The coordinate points of the AA group and the treatment group are also distinguished to a certain extent, indicating that the patients with aplastic anemia have obvious recovery after CsA treatment. Figure 2 C-2D, the characteristic value of the effect of metabolites on the blank group is the horizontal axis, and the correlation between the sample score and the metabolite is the vertical axis. The metabolites are very close to the upper right corner and the lower left corner in the figure, indicating that their differences are of obvious significance.

[0077] OPLS-DA is a supervised discriminant analysis method that can maximize the identification of overall differences in metabolic profiles between groups and can find potential biomarkers between groups based on VIP>1. Figure 3 A-3D, compared with AA patients, patients treated with CsA showed a trend towards the blank group, indicating that CsA can regulate the metabolic disorders caused by AA. At the same time, the OPLS-DA results showed that the plasma metabolic profiles between different groups were significantly separated. Response permutation testing (RPT) can be used to evaluate the accuracy of the model to avoid the classification obtained by the supervised learning method is not accidental. Figure 3 E-3F, 200 RPTs were performed on the OPLS-DA model, and R2 = 0.834 and Q2 = -0.734 were obtained under LC-MS detection, and R2 = 0.775 and Q2 = -0.459 were obtained under GC-MS detection, indicating that the model did not produce overfitting.

[0078] 2.3 Screening of differential metabolites and pathway enrichment analysis

[0079] In OPLS-DA and PLS-DA analysis, VIP value can be used to measure the strength and explanatory power of metabolite expression pattern for sample classification and identification of biologically significant differential metabolites. The screening criteria are: VIP>1 and P<0.05, which are considered as potential differential metabolites. Metabolic pathway analysis and matching based on KEGG database can obtain the pathways of differences between AA group and healthy control group, and between treatment group and AA group.

[0080] 2.3.1 Comparative analysis between AA group and healthy control group

[0081] As shown in Figure 4A, in positive and negative ion modes, LC-MS and GC-MS data were combined for analysis, and 167 differential metabolites were identified between the AA group and the healthy control group, of which 58 were upregulated and 109 were downregulated, including phospholipids, amino acids, saturated or unsaturated fatty acids, etc. The expression levels of all significantly different metabolites were hierarchically clustered and a heat map was drawn, as shown in Figure 4A. Figure 4 As shown in B. Figure 4 C displays metabolites and their FC values ​​more intuitively. Figure 4 As shown in D, the Pearson correlation coefficient was used to measure the correlation between two metabolites, and the correlation analysis was performed on the top 20 significantly differential metabolites of VIP. The KEGG online database (https: / / www.genome.jp / kegg / ) was used to perform pathway enrichment analysis on differential metabolites. Compared with the healthy control group, the differences in the AA group mainly involved pyrimidine metabolism, galactose metabolism, pantothenate and CoA biosynthesis, AMPK and FoxO signaling pathways.

[0082] 2.3.2 Comparative analysis between AA group and treatment group

[0083] As shown in Figure 5A, in positive and negative ion modes, LC-MS and GC-MS data were combined for analysis, and 26 differential metabolites were identified between the AA group and the treatment group, of which 11 were upregulated and 15 were downregulated. The expression levels of all significantly different metabolites were hierarchically clustered and a heat map was drawn, as shown in Figure 5A. Figure 5 As shown in B. Figure 5 C displays metabolites and their FC values ​​more intuitively. Figure 5 As shown in D, the Pearson correlation coefficient was used to measure the correlation between two metabolites, and the correlation analysis was performed on the top 20 significantly different metabolites of VIP. The KEGG online database was used to perform pathway enrichment analysis on the differential metabolites. Compared with the treatment group, the differences in the AA group mainly involved signaling pathways such as pyrimidine metabolism, pantothenate and CoA biosynthesis, linoleic acid / α-linoleic acid metabolism, and beta-Alanine metabolism.

[0084] The metabolites that were significantly different between the healthy control group and the AA group and could be significantly adjusted after CsA treatment were considered differential metabolites, totaling 9, namely uracil, PS (21:0 / 22:6), PC (38:6), 1-Octadecene, cytidine monophosphate, and inosinic acid, as shown in Table 1. The differential metabolites mainly involved pyrimidine metabolism and purine metabolism, suggesting that CsA may improve AA by regulating the above metabolic pathways.

[0085] Table 2 Identification results of differential metabolites in plasma of patients with aplastic anemia

[0086]

[0087] Compared with the related art, the biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia provided by the present invention has the following beneficial effects:

[0088] The present invention provides a biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia.

[0089] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A biomarker method for predicting the efficacy of cyclosporine in treating aplastic anemia, characterized in that: The following steps are involved: S1. Select healthy people and newly diagnosed patients with aplastic anemia and patients in stable stage of the disease, and establish a healthy control group (blank group), a model group (AA group), and a treatment group to form a research model; S2. Collect biological samples from healthy people, newly diagnosed patients with aplastic anemia, and patients in the stable stage of the disease, wherein the biological samples are blood samples; S3. Perform metabolomics studies and analyses of biological samples, including liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) analysis, to identify and quantify metabolites; S4. Use multivariate statistical methods to screen out metabolites that are differentially expressed in patients with aplastic anemia compared with healthy controls; S5. Identify differential metabolites associated with the therapeutic efficacy of cyclosporine (CsA); S6. Based on the expression levels of the above-mentioned differential metabolites, predict the efficacy of cyclosporine in the treatment of aplastic anemia.

2. The method for predicting the biomarker efficacy of cyclosporine in treating aplastic anemia according to claim 1, characterized in that: The inclusion and exclusion criteria for the study model selection were: The following criteria were used when selecting medical records: ① patients diagnosed with AA for the first time and without other blood diseases; ② no history of endocrine system diseases; plasma samples were obtained from the secondary use of patients' blood routine samples; Exclusion criteria: ① patients with severe heart, lung, kidney and other important organ dysfunction; ② patients who received blood transfusion during treatment; ③ patients with malignant tumors; ④ patients with mental and intellectual disabilities; ⑤ patients who were breastfeeding or pregnant.

3. The method for predicting the efficacy of cyclosporine in treating aplastic anemia according to claim 1, characterized in that: The metabolomics study of the biological sample comprises the following steps: P1. Sample processing, including liquid sample analysis and gas phase sample processing; P2. Chromatographic and mass spectrometric conditions: P2.1, LC-MS conditions: Chromatographic column: ACQUITY UPLC HSS T3 (100 mm × 2.1 mm, 1.8 μm); mobile phase: phase A is water (containing 0.1% formic acid), phase B is acetonitrile; flow rate: 0.35 mL / min; injection volume: 3 μL; column temperature: 35 °C; gradient elution program: 0-2.0 min 5% B, 2.0-4.0 min 5-35% B, 4.0-8.0 min 35%-55% B, 8.0-10.0 min 55-75% B, 10.0-14.0 min 75-95% B, 14.0-15.0 min 95% B; mass spectrometry conditions: positive ion and negative mode, primary scan range of m / z: 100-1500, scanning collision mode of high energy collision dissociation (HCD), heater temperature of 320°C, electrospray voltage of 3.5KV, capillary temperature of 350°C; P2.2, GC-MS conditions: The DB-5MS capillary column (30m×0.25mm×0.25μm) was used as carrier gas with high-purity helium; the temperature was programmed as follows: the initial temperature was 60°C, maintained for 0.5 min; the temperature was programmed to 125°C at 8°C / min; the temperature was programmed to 210°C at 8°C / min; the temperature was programmed to 270°C at 15°C / min; the temperature was programmed to 305°C at 20°C / min and maintained for 5 min. Flow rate 1.0mL / min, injection port temperature: 260℃. Injection volume: 2μL. Ion source: electron impact source (EI), source temperature: 230℃, electron energy: 70eV, quadrupole temperature: 150℃. Scan mode: full scan mode (SCAN), mass scan range: m / z 50-500; P3. Data processing: Metabolomics processing software Progenesis QI and MS-DIAL were used to perform baseline filtering, retention time correction, chromatographic peak identification, alignment, and normalization on the raw data; The substances were identified by retention time and ion fragments in combination with the Thermo mzCloud online database, Thermo mzValut local database, and ChemSpider database. The normalized peak areas were subjected to principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA). The differential metabolites between the AA group and the blank group, and between the AA group and the treatment group were screened under the conditions of VIP (variable importance in the projection)>1.0 and P<0.05, and the intervention of CsA was observed. MetaboAnalyst 5.0 (http: / / www.metaboanalyst.ca / ) was used to analyze the differential metabolite data and conduct metabolic pathway enrichment analysis.

4. The method for predicting the biomarker efficacy of cyclosporine in treating aplastic anemia according to claim 3, characterized in that: The liquid sample analysis comprises the following steps: taking out a plasma sample stored at -80°C, thawing it in a 4°C environment, transferring 200 μL of plasma into a 1.5 mL EP tube; adding 800 μL of methanol-acetonitrile (V:V=2:1) ​​for protein precipitation, vortexing for 1 minute; centrifuging for 10 minutes (12000 rpm, 4°C), transferring a portion of the supernatant to a liquid injection vial for LC-MS analysis, and placing another portion of the supernatant into a glass derivatization vial for GC-MS detection; The gas phase sample processing comprises the following steps: taking a glass derivatization vial containing a supernatant, and using a centrifugal concentrator to evaporate the sample; adding 80 μL of a 15 mg / mL trimethoprim-pyridine hydrochloride solution to the vial, and performing an oximation reaction in a 37° C. shaking incubator for 60 minutes; then adding 50 μL of a BSTFA derivatization reagent and 20 μL of n-hexane, and reacting at 70° C. for 60 minutes; and performing GC-MS detection after standing at room temperature.

5. The method for predicting the biomarker efficacy of cyclosporine in treating aplastic anemia according to claim 1, characterized in that: Performing metabolomics analysis on the biological sample comprises the following steps: Q1. Data quality evaluation; Q2. Multivariate statistical analysis; Q3. Screening of differential metabolites and pathway enrichment analysis, including comparative analysis between the AA group and the healthy control group, and comparative analysis between the AA group and the treatment group.

6. The method for predicting the biomarker efficacy of cyclosporine in treating aplastic anemia according to claim 1, characterized in that: The differential metabolites include uracil, PS (21:0 / 22:6), PC (38:6), 1-Octadecene, cytidine monophosphate and inosinic acid, cytidine monophosphate and inosinic acid.

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