Application of acute leukemia metabolic marker

Abstract

The invention relates to the technical field of analysis and detection, in particular to application of an acute leukemia metabolic marker. According to the method, the metabolic markers related to acute leukemia are screened based on a urine sample, the metabolic markers can reflect metabolic changes of cells, an acute leukemia sample and a healthy sample can be well distinguished, the occurrence risk of acute leukemia can be accurately evaluated, operation is easy, rapid and efficient, sample pretreatment is easy, and the method is suitable for large-scale popularization and application. The method can be carried out only by providing a urine sample, other tissue samples are not needed, the possibility and feasibility of clinical application are improved, and a basis is provided for early diagnosis, disease monitoring, disease prognosis and treatment response evaluation of acute leukemia.

Description

Technical Field

[0001] The invention relates to the technical field of analysis and detection, and in particular to the application of acute leukemia metabolic markers. Background Art

[0002] Leukemia is a malignant tumor of the hematopoietic system, also known as blood cancer. It is caused by DNA mutation of stem cells in the bone marrow, which leads to abnormal function of hematopoietic tissue, excessive production of immature white blood cells, hindering other work of the bone marrow, and reducing the function of the bone marrow to produce other blood cells. According to the degree of cell differentiation and maturity and the natural course of the disease, leukemia can be divided into acute leukemia and chronic leukemia. In acute leukemia, cell differentiation stagnates in the early stage, the disease progresses rapidly, and the natural course is only a few months. In chronic leukemia, cell differentiation stagnates in the later stage, the disease progresses relatively slowly, and the natural course is several years. According to the main affected cell series, it can be divided into lymphocytic leukemia and non-lymphocytic (myeloid) leukemia. If patients with acute leukemia do not receive systematic and regular treatment, the average survival period is only about 3 months. As an extremely complex disease, acute leukemia has not yet been fully studied and its pathogenesis mechanism has not yet been fully understood. There are still many issues that need to be explored in clinical practice.

[0003] As a complex disease with a wide variety of types and diverse pathogenesis, the etiology of leukemia has not yet been fully understood. Therefore, both at home and abroad, efforts have been made to study molecular markers for early diagnosis, typing, and prognosis evaluation of leukemia. At present, the diagnosis of acute leukemia mainly relies on blood cell examination, bone marrow biopsy, and the doctor's clinical subjective judgment. Because the above methods have many limitations, they are prone to misdiagnosis and missed diagnosis. In recent years, it has been found that the expression levels of some genes and proteins have obvious changes in the pathology and development of leukemia. Some have been proved to be biomarkers of leukemia through experiments and verification. For example, FLT3 (Fms-like Tyrosine Kinase 3) is a receptor tyrosine kinase located on the cell membrane and belongs to the receptor tyrosine kinase family. In normal cells, FLT3 is mainly expressed in hematopoietic stem cells and certain white blood cells. It plays an important role in the blood system, especially in regulating cell proliferation and differentiation in the hematopoietic process. It is widely and highly expressed in leukemia, especially in acute myelogenous leukemia (AML), where the expression rate is as high as 70%~100%. The positive expression rate in acute lymphocytic leukemia (ALL) is also very high. Early studies have confirmed that it can be used as a potential molecular marker. It is now widely believed to be a molecular marker that provides a basis for the diagnosis and prognosis of leukemia, but existing reports lack research on metabolic markers.

[0004] Therefore, finding metabolic markers for the identification or diagnosis of acute leukemia has become a technical problem that urgently needs to be solved in this field. Summary of the invention

[0005] In order to solve the above technical problems, the present invention applies UPLC-QTOF-MS technology, detects the metabolites in the urine of healthy people and acute leukemia people, and then accurately finds the differential metabolites between the two groups of samples through multivariate statistical analysis of multiple samples between the two groups. Based on this, the following technical solution is proposed.

[0006] In a first aspect, the present invention provides the use of a reagent for detecting the content of a metabolite marker in the preparation of a product for identifying acute leukemia; the metabolite marker is at least one of creatinine, homoserine, eicosapentaenoic acid, shikimic acid, citric acid, salicylic acid, sebacic acid, and methionine; and the reagent is a reagent for detecting the content of the metabolite marker in a subject or a sample from a subject.

[0007] In a second aspect, the present invention provides the use of a reagent for detecting the content of a metabolite marker in the preparation of a product for diagnosing, monitoring, evaluating treatment response or judging the prognosis of acute leukemia; the metabolite marker is at least one of creatinine, homoserine, eicosapentaenoic acid, shikimic acid, citric acid, salicylic acid, sebacic acid and methionine; and the reagent is a reagent for detecting the content of the metabolite marker in a subject or a sample from a subject.

[0008] In some embodiments, the acute leukemia is classified as AML, AML-M2, AML-M4 or AML-M5.

[0009] In some embodiments, the sample from the subject is a urine sample.

[0010] In some embodiments, the product includes: a chip, a reagent, a test paper, a drug, a preparation, a kit, or a high-throughput screening platform.

[0011] At the same time, the present invention provides the use of the metabolic marker in at least one of the following aspects: (1) Preparation of products for identification of acute leukemia; (2) Preparation of products for diagnosis, monitoring, evaluation of treatment response or prognosis of acute leukemia.

[0012] In a third aspect, the present invention provides the use of a regulator of a metabolic marker content in the preparation of a drug for treating acute leukemia; the metabolic marker is at least one of creatinine, homoserine, eicosapentaenoic acid, shikimic acid, citric acid, salicylic acid, sebacic acid, and methionine.

[0013] In a fourth aspect, the present invention provides the use of a regulator of a metabolic marker content and at least one therapeutic agent in the preparation of a medicament for treating acute leukemia; the metabolic marker is at least one of creatinine, homoserine, eicosapentaenoic acid, shikimic acid, citric acid, salicylic acid, sebacic acid, and methionine.

[0014] In a specific implementation process, the regulator is an agonist or a promoter.

[0015] In the specific implementation process, the therapeutic agent is a chemotherapy drug, a small molecule targeted drug, an immunotherapy drug or a Chinese patent medicine.

[0016] In a fifth aspect, the present invention provides a product, which contains a detection reagent or regulator for the content of a metabolic marker; the metabolic marker is at least one of creatinine, homoserine, eicosapentaenoic acid, shikimic acid, citric acid, salicylic acid, sebacic acid, and methionine.

[0017] In a specific implementation process, the regulator is an agonist, a promoter, an inhibitor or an antagonist.

[0018] In the specific implementation process, the product includes: a chip, a reagent, a test paper, a drug, a preparation, a test kit or a high-throughput screening platform.

[0019] In a sixth aspect, the present invention provides a method for screening candidate drugs for treating acute leukemia, comprising: detecting the content of metabolic markers in samples of subjects before and after treatment with the candidate drugs; the metabolic markers are at least one of creatinine, homoserine, eicosapentaenoic acid, shikimic acid, citric acid, salicylic acid, sebacic acid, and methionine.

[0020] If the level of metabolic markers in the sample of the subject increases after treatment with the candidate drug compared to before treatment, it means that the candidate drug is effective in treating acute leukemia.

[0021] Preferably, the sample from the subject is urine.

[0022] In some embodiments, the metabolite marker in the urine of healthy people is higher than the critical peak value, and the metabolite marker in the urine of acute leukemia patients is lower than the critical peak value.

[0023] Compared with the prior art, the present invention has the following beneficial effects: The present invention screens out metabolic markers related to acute leukemia based on urine samples. These metabolic markers can reflect the metabolic changes of cells, can well distinguish acute leukemia samples from healthy samples, and can accurately evaluate the risk of acute leukemia. The operation is simple, rapid, and efficient, the sample pre-treatment is simple, and only a urine sample is required, without the need for other tissue samples, thereby improving the possibility and feasibility of clinical application and providing a basis for early diagnosis of acute leukemia, disease monitoring, disease prognosis, and treatment response evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a classification diagram for identifying metabolites.

[0025] Figure 2 It is the PCA score plot of metabolites in the acute leukemia group and the healthy control group.

[0026] Figure 3 It is the OPLS-DA score graph of metabolites in the acute leukemia group and the healthy control group.

[0027] Figure 4 This is the OPLS-DA permutation test graph of metabolites in the acute leukemia group and the healthy control group. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In the examples provided in this specification, if no specific techniques or conditions are specified, the techniques or conditions described in the literature in this field or the product instructions are used. If the manufacturer of the reagents or instruments used is not specified, they are all conventional products that can be purchased through regular channels.

[0030] Example 1 Screening of metabolic markers for acute leukemia 1. Materials and reagents Urine from 15 adults with acute leukemia (acute leukemia group, denoted as AL group) and urine from 9 healthy adults (healthy control group, denoted as HC group) were used.

[0031] Methanol, acetonitrile, and ammonium acetate were purchased from Thermo Fisher Scientific, Inc. All chemicals and solvents were of analytical grade or mass spectrometry grade.

[0032] 2. Instruments and equipment Triple TOF 6600 LC / MS instrument, ABsCIEX, USA; SCI-FS MX-F vortex mixer, Shanghai Jinlan Instrument Manufacturing Co., Ltd.; MiLLi-Q ultrapure water system, Merck Millipore China Co., Ltd.; ACQUITYUPL ChhSS T3 (1.8µm, 2.1×100mm), Waters, USA; 3K15 high-speed centrifuge, SIGMA, Germany.

[0033] 3. Methods 3.1 Pretreatment of urine samples The urine sample was taken out of the -80℃ refrigerator and slowly thawed at 4℃. After the sample was thawed, it was vortexed for 30s to mix, centrifuged at 10000r / min and 4℃ for 10min, and 500µL of the supernatant was mixed with an equal volume of methanol into an EP tube to precipitate the protein. After vortexing for 5min, it was placed in a -20℃ refrigerator for 2h, centrifuged at 10000r / min for 10min at 4℃, and all the supernatant was filtered through a 0.22µm filter membrane into a new 1.5mL EP tube. 200µL was taken and placed in a 1.5mL injection bottle containing an inner liner tube for analysis on the machine. In addition, an equal amount of supernatant from all samples was mixed as a quality control sample for on-machine testing. In order to monitor the repeatability and stability of the analysis process, a quality control sample was inserted into every 6 test analysis samples during the detection process.

[0034] 3.2 UPLC-QTOF-MS detection conditions Chromatographic separation was performed using an ACQUITY UPLChSS T3 (1.8µm, 2.1×100mm) column. The mobile phase was solvent A (aqueous solution containing 5mM ammonium acetate) and solvent B (acetonitrile), with gradient elution (0-10min, 5-90%B; 10-12min, 90%B; 12-12.5min, 90-5%B; 12.5-15min, 5%B). The column temperature was set at 35°C, the flow rate was 0.35mL / min, and the injection volume was 5µL.

[0035] Mass spectrometry analysis was performed using a quadrupole time-of-flight mass spectrometer in positive and negative ion, IDA scanning mode, with a positive ion spray voltage of 5500 kV and a negative ion spray voltage of -4500 kV. The electrospray ionization source (ESI) had an ion source temperature of 550 °C, a scan range of 50-1200 m / z, ion source gas I (GS1) 60 psi, gas II (GS2) 60 psi, curtain gas (CUR) 35 psi, and a delustering potential (DP) of 70 V.

[0036] 3.3 LC-MS spectrum processing After converting the original data format (.wiff) to MzXML format, ProteoWizard was used for processing. XCMS was used to perform baseline filtering, peak extraction, peak alignment, peak matching, and peak intensity correction on the acquired spectra, and the results were converted into a csv format data set containing the retention time, mass-to-charge ratio, and corresponding ion intensity information of the compound. Based on databases such as Human Metabolome Database (HMDB), Lipid maps, and Metlin, the mass error was set to 5ppm, and the metabolites were identified in combination with the secondary mass spectrometry matching score.

[0037] 3.4 Data Analysis The qualitative information after the above processing was screened according to the principle that the proportion of each extracted ion chromatographic peak detected in the quality control sample was not less than 80%. After the screened data was normalized, SPSS27.0 was used for statistical analysis, and independent sample t-test was used for inter-group comparison. P <0.05 indicated that the difference was statistically significant. On this basis, multivariate data analysis was performed, including unsupervised principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA), to find differential metabolites. The screening criteria were: variable importance for the projection (VIP) > 1, fold change (FC) > 1.5 or FC < 0.5, P <0.05. Pearson correlation coefficient was used for correlation analysis to measure the linear correlation between two metabolites. Finally, KEGG ID of differential metabolites was used for pathway enrichment analysis, and the significantly enriched metabolic pathways were analyzed by hypergeometric test ( P ≤0.05) for non-targeted metabolomics studies.

[0038] 4. Results and Analysis 4.1 Spectral analysis Using UPLC-QTOF-MS technology, metabolic information of urine samples was collected in positive and negative ion modes. The database identification results showed that a total of 903 metabolites were identified after the peaks in the positive and negative ion modes were merged, of which 614 metabolites were identified in the positive ion mode and 289 metabolites were identified in the negative ion mode. They were statistically analyzed according to the chemical taxonomy information (such as Figure 1The results showed that the main categories of metabolites identified included organic heterocyclic compounds (17%), lipids and lipid molecules (23%), organic acids and their derivatives (23%) and benzene compounds (14%).

[0039] 4.2 PCA analysis In order to explore the changes in the main components of urine in the acute leukemia group and the healthy control group, the PCA method with dimensionality reduction was used for multivariate statistical analysis to intuitively reflect the differences in metabolites between samples. The PCA results showed that ( Figure 2 ), all samples are within the 95% confidence interval, indicating that the two groups of urine samples can be distinguished, there are differences between the groups, but the differences within the groups are large. This may be because PCA, as an unsupervised analysis method, cannot ignore the differences within the group or eliminate random errors that are not related to the research purpose, which sometimes leads to the failure to find differences between the groups. In order to conduct in-depth analysis, a supervised method is needed to further analyze the data.

[0040] 4.3 Orthogonal Partial Leastsquares-Discriminant Analysis (OPLS-DA) Since OPLS-DA is supervised and can reduce intra-group differences and amplify inter-group differences, this experiment uses the OPLS-DA model to process the sample data and obtain the corresponding score map ( Figure 3 )、Permutation test result chart( Figure 4 ), the results show that there are obvious differences in the sample points between the two groups. In the positive and negative ion merging mode, R 2 X=0.44, R 2 Y=0.96,Q 2 =0.95(R 2 Y is the explanatory power of the model for the Y variable, Q 2 is the predictability of the model), the R 2 Y and Q 2 All of them are above 0.5, indicating that the model has good stability. The OPLS-DA score graph shows that the urine metabolic profiles of the experimental group and the control group clearly present a bipolar distribution, indicating that there are significant differences in the urine metabolites of the two groups. On this basis, the variable important in projection (VIP) of each metabolite is accurately calculated using bioinformatics methods to provide data basis for further screening of differential metabolites.

[0041] In order to check whether there is an overfitting problem in the OPLS-DA model, a permutation test is performed, also known as the cross-validation test of the OPLS-DA model. The test principle is: redistribute the category labels of the samples and construct a permuted grouping method to verify the quality and stability of OPLS-DA modeling. Figure 4 This is the OPLS-DA permutation test diagram of the acute leukemia group and the healthy control group under the positive and negative ion combined mode. The horizontal axis represents the similarity with the model, and the vertical axis represents R 2 Y and Q 2 Y. R 2 The closer the Y value is to 1, the better the model fit and the better the discrimination. 2 Y=0.96, indicating that the model has good discrimination, and the difference between the acute leukemia group and the healthy control group is more. 2 The Y value is 0.95, indicating that the model has good predictive ability. The above results confirm that the OPLS-DA model does not have overfitting phenomenon, the model has good discrimination ability, the analysis results are reliable and has good predictive ability.

[0042] 4.4 Screening of potential markers Based on the results of OPLS-DA analysis, VIP>1.00, fold change (FC)>1.5 or FC<0.5, and P<0.05 were used as the thresholds for screening differential metabolites. By analyzing the metabolites in both positive and negative ion modes of the acute leukemia group and the healthy control group, 92 significant differential metabolites were obtained, including 52 detected in the positive ion mode and 40 in the negative ion mode. Compared with the healthy control group, 14 differential metabolites were found to be elevated in the acute leukemia group, and 78 were found to be decreased in the negative ion mode. The specific information is shown in Table 1. These metabolites were classified using the HMDB database, mainly including organic acids and their derivatives, lipids and lipid molecules, organic heterocyclic compounds, and benzene compounds.

[0043] After a detailed analysis of the differential metabolites in Table 1, it was found that many exogenous metabolites were included (such as drug metabolites that showed higher levels in the acute leukemia group). After excluding the above factors, markers were screened out by combining the difference significance and the difference multiple, including: creatinine, homoserine, eicosapentaenoic acid, shikimic acid, citric acid, salicylic acid, sebacic acid, and methionine. Their relative levels in the acute leukemia group were lower than those in the healthy control group.

[0044] Table 1 Differential metabolites and related information between acute leukemia group and healthy control group

[0045] 4.5 KEGG pathway enrichment analysis and metabolic network construction of differential metabolites MetaboAnalyst5.0 online analysis software was used to analyze the metabolic pathways of all differential metabolites, and enrichment analysis and topological analysis were performed after database matching to screen out important metabolic pathways related to differential metabolites. The results showed that the differential metabolites between the acute leukemia group and the healthy control group were mainly enriched in the biosynthesis of steroid hormones; alanine, aspartate and glutamate metabolism; niacin and nicotinamide metabolism; citric acid cycle (TCA cycle); glutathione metabolism; glycerophospholipid metabolism; tryptophan metabolism and other metabolic pathways, and the number of differential metabolites hit in all metabolic pathways was 41.

[0046] 4.6 Determination of biomarkers The receiver operating characteristic curve was drawn to determine the diagnostic effect of potential biomarkers; the Youden index was used to screen the determined biomarkers; and the biomarkers and their peak area critical values ​​were determined. The results are shown in Table 2. The AUC values ​​were all greater than 0.7, which means that the above substances have good discrimination ability and can effectively distinguish the two types of samples to a certain extent. The sensitivity and specificity values ​​were obtained through analysis, and the Youden index was calculated to find the optimal diagnostic cutoff peak.

[0047] Table 2 AUC values, sensitivity, specificity and Youden index of biomarkers

[0048] It can be seen that the above biomarkers have good sensitivity and specificity for identifying acute leukemia.

[0049] Example 2 Validation of biomarkers for identifying acute leukemia This example uses 15 urine samples from acute leukemia patients (AL) and 9 urine samples from healthy people (HC), which are different from those in Example 1, and uses the detection and analysis method of Example 1 to verify the reliability of biomarkers for identifying acute leukemia. The results are shown in Table 3.

[0050] Table 3 Verification results

[0051] It can be seen that the peak values ​​of urine validation samples from patients with acute leukemia are all lower than the critical peak value, while the peak values ​​of urine validation samples from healthy people are all higher than the critical peak value, indicating that the biomarkers obtained by screening in Example 1 are reliable for identifying acute leukemia.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Use of a reagent for detecting the content of a metabolite marker in the preparation of a product for identifying acute leukemia; the metabolite marker is at least one of homoserine, eicosapentaenoic acid, shikimic acid, and sebacic acid; the reagent is a reagent for detecting the content of the metabolite marker in a subject or a sample from a subject.

2. Use of a reagent for detecting the content of a metabolite marker in the preparation of a product for diagnosing, monitoring, evaluating treatment response or judging the prognosis of acute leukemia; the metabolite marker is at least one of homoserine, eicosapentaenoic acid, shikimic acid and sebacic acid; the reagent is a reagent for detecting the content of the metabolite marker in a subject or a sample from a subject.

3. The use according to claim 1 or 2, characterized in that: The acute leukemia is classified into AML, AML-M2, AML-M4 or AML-M5.

4. The use according to claim 1 or 2, characterized in that: The sample of the subject is a urine sample.

5. The use according to claim 1 or 2, characterized in that: The products include: chips, reagents, test strips, drugs, preparations, kits or high-throughput screening platforms.

6. Use of a regulator of metabolic marker content in the preparation of a drug for treating acute leukemia; the metabolic marker is at least one of homoserine, eicosapentaenoic acid, shikimic acid, and sebacic acid.

7. Use of a regulator of metabolic marker content and at least one therapeutic agent in the preparation of a drug for treating acute leukemia; the metabolic marker is at least one of homoserine, eicosapentaenoic acid, shikimic acid, and sebacic acid.

8. The use according to claim 6 or 7, characterized in that: The modulator is an agonist or a promoter.

9. A product, characterized in that It contains a detection reagent or regulator for the content of a metabolic marker; the metabolic marker is at least one of homoserine, eicosapentaenoic acid, shikimic acid, and sebacic acid.

10. A method for screening candidate drugs for treating acute leukemia, characterized in that: include: The content of metabolic markers in the samples of the subjects before and after the candidate drug treatment is detected; the metabolic marker is at least one of homoserine, eicosapentaenoic acid, shikimic acid, and sebacic acid.

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