Method for exploring cause of A-supply O bone marrow transplantation failure of acute myelogenous leukemia

Abstract

The invention belongs to the technical field of biological medicines, and particularly discloses a method for exploring an acute myelogenous leukemia A-donor O bone marrow transplantation failure reason, which is intended to perform sequencing verification from database computational biology analysis to sample proteomics, and adopts a mode of combining computational biology and in-vivo research to research the A-donor O bone marrow transplantation failure reason of acute myelogenous leukemia. From biological analysis of open samples in a database to collection of hematopoietic stem cell transplantation failure samples for protein sequencing and verification, biological characteristics of juvenile acute myelogenous leukemia and proteomics characteristics of treatment failure and relapse can be comprehensively understood, and a bone marrow transplantation treatment failure mechanism can be determined. And guiding significance is provided for optimization of a bone marrow transplantation treatment scheme.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a method for exploring the cause of failure of A-donor-O bone marrow transplantation in acute myeloid leukemia. Background Art

[0002] Acute myeloid leukemia (AML) is a group of heterogeneous malignant clonal blood diseases, whose pathophysiological characteristics are the massive proliferation of abnormal blood cells in the bone marrow and other hematopoietic tissues, infiltration of various tissues, and affecting normal hematopoietic function. According to the FAB (French-American-British) classification, acute myeloid leukemia is currently divided into eight subtypes, M0-M7, of which M2 is the mature granulocyte type of acute myeloid leukemia. Bone marrow transplantation is a common treatment for acute myeloid leukemia M2, but transplant failure is a more difficult problem in the treatment process. Therefore, it is crucial to explore the causes of bone marrow transplant failure in acute myeloid leukemia for the treatment of acute myeloid leukemia M2. Summary of the invention

[0003] In view of the above problems, the present invention provides a method for exploring the causes of failure of bone marrow transplantation from A to O in acute myeloid leukemia. The method intends to obtain data from a database for bioinformatics analysis, construct a prediction model, and then perform proteomics group verification. By combining computer technology and basic experiments, the method completely reveals the biological characteristics of adolescent acute myeloid leukemia and the biological effects of bone marrow transplantation failure from the perspectives of modern biotechnology such as data mining and simulation prediction, as well as protein blot expression, thereby promoting the selection of individualized and precise plans for adolescent acute myeloid leukemia, and providing conclusive evidence for reducing patients' medical expenses and treatment risks.

[0004] The technical solution of the present invention is as follows:

[0005] A method for exploring the cause of failure of A-donor O bone marrow transplantation for acute myeloid leukemia, comprising the following steps: based on the GTEx and TCGA databases, combined with case samples of failed A-donor O bone marrow transplantation for acute myeloid leukemia M2, bioinformatics analysis is performed to construct a prediction model, the biological characteristics of acute myeloid leukemia and the prediction of overall survival rate are comprehensively analyzed, and then proteomic detection and analysis are used to explore the biological mechanism of failure of adolescent children with acute myeloid leukemia receiving A-donor O bone marrow transplantation from direct relatives.

[0006] The present invention collects peripheral blood samples from adolescent acute myeloid leukemia patients who have failed bone marrow transplantation from A-donor O-donors and their immediate family members (healthy donors), and uses database analysis and proteomics sequencing technology to find through differential protein enrichment analysis that there are significant differences in the expression of SERPI NG1, SERPI NB1 and PRG2 proteins, as well as a decrease in the phosphorylation level of the MEPK pathway, and the neutrophil extracellular trap formation pathway is involved in the biological behavior of the disease.

[0007] It was further concluded that the failure of bone marrow transplantation in adolescents with acute myeloid leukemia receiving direct relatives was related to abnormal protein expression and induced functional changes of different pathways.

[0008] In a further technical solution, the process of bioinformatics analysis, prediction model building and proteomics detection and analysis specifically includes the following steps:

[0009] S1. Obtain acute myeloid leukemia bone marrow samples and normal whole blood samples based on GTEx and TCGA databases, perform bioinformatics analysis, and obtain the gene expression levels in each sample;

[0010] S2. Construct a 3-5 year overall survival prediction model based on the analyzed data and estimate the overall survival rate;

[0011] S3. Perform differential protein analysis of test samples using proteomics 4D-DIA technology to screen out key differential proteins and signal pathway proteins;

[0012] S4. Verify the expression differences of important proteins and signaling pathways based on Western blotting technology.

[0013] In a further technical solution, normal whole blood samples were obtained from the GTEx database, and acute myeloid bone marrow samples and RNA transcriptome profiles of patients with acute myeloid leukemia under 18 years old were obtained from the TCGA database.

[0014] In a further technical solution, in steps S3-S4, the process of detecting and analyzing the test samples specifically includes the following steps: collecting peripheral blood serum samples from patients with acute myeloid leukemia M2 under the age of 18 and their father's donors, analyzing the differential protein expression and pathway impact between samples through 4D-DIA proteomics technology, and clarifying the different biological functional responses of the samples through differential proteins, and screening the target proteins and signal pathways based on the differential proteins for protein blotting verification.

[0015] In a further technical solution, the process of differential protein and pathway western blot analysis specifically includes the following steps:

[0016] The serum of patients and their families was de-albuminized to verify differential protein expression;

[0017] Among them, serum albumin removal: serum albumin kit removes albumin from samples to facilitate the implementation of western blot experiments;

[0018] Western blotting technology was used to verify the differential protein and signaling pathway protein expressions;

[0019] In a further technical solution, the serum sample from which albumin is removed is subjected to western-blot detection.

[0020] The beneficial effects of the present invention are:

[0021] The present invention intends to analyze the expression levels of genes in 151 acute myeloid bone marrow samples and 456 normal whole blood samples in the public database TCGA database and GTEx, construct a 3-5 year overall survival prediction model, and then collect samples of failed bone marrow transplants from immediate relatives for mechanism research, comprehensively reveal the biological characteristics and 3-5 year survival model of adolescent acute myeloid leukemia, as well as the impact of differential protein expression in samples of AML relapse after failed bone marrow transplantation, clarify the proteomic changes in samples of failed bone marrow transplantation, and fully evaluate the biological characteristics of such patients and the choice of treatment options. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the overall design concept of the embodiment of the present invention;

[0023] Figure 2 It is the database mining gene function enrichment map in Example 1 of the present invention;

[0024] Figure 3 It is a diagram for constructing a prediction model in Example 2 of the present invention;

[0025] Figure 4 This is a differential protein analysis diagram of the protein sequencing of the samples collected in Example 3 of the present invention;

[0026] Figure 5 This is a verification analysis diagram of differentially expressed proteins in samples collected in Example 4 of the present invention. DETAILED DESCRIPTION

[0027] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0028] This example uses database sample bioinformatics analysis to construct a prediction model for the overall survival rate of adolescent acute myeloid leukemia and collect samples to verify the design ideas of differential protein expression. Figure 1 As shown in the figure, the bone marrow samples of adolescent acute myeloid leukemia were enriched in neutrophil activation, neutrophil degranulation, immune response of BP, cytoplasmic cyst cavity of CC, secretory granule cavity and immunoglobulin binding and chromatin binding of MF through the previous database analysis. The predicted value of the 3-5 year overall survival rate was consistent with the observed value, and the prediction model was successfully constructed.

[0029] Further research found that in the peripheral blood samples of a 12-year-old O-type AML-M2 patient who relapsed after a half-matched transplant of bone marrow combined with peripheral blood hematopoietic stem cells from his A-type father, and the peripheral blood samples of his father (healthy donor), there were 295 unique proteins in the patient. Differential protein enrichment analysis found that there were significant differences in the expression of SERPING1, SERPINB1 and PRG2 proteins, and MAPK and neutrophil extracellular trap formation pathways were involved in the biological behavior of the disease.

[0030] Embodiment 1:

[0031] Identification of DEGs and gene function enrichment:

[0032] Expression levels of genes in 151 acute myeloid bone marrow samples from TCGA and 456 normal whole blood samples from GTEx. The data from TCGA and GTEx were merged and normalized using the “l imma” R package. Among the 17,194 genes, 2,679 were significant. The information of DEGs was applied to GO analysis. Figure 2 As shown, DEGs were enriched in neutrophil activation, neutrophil degranulation, immune response of BP, cytoplasmic cystic cavity of CC, secretory granule cavity and immunoglobulin binding and chromatin binding of MF.

[0033] Embodiment 2:

[0034] Constructing a 3-5 year prediction model for overall survival (OS):

[0035] A model of adolescent acute myeloid leukemia was constructed based on the database results, such as Figure 3 As shown, Figure 3 A analyzed the predicted nomination graph and estimated the 3-5 year OS rate based on the score ratio at the bottom of the nomination graph. Figure 3 As shown in B, the AUC of 3-5 year survival rate can reach around 0.7, confirming that the prediction model has high accuracy.

[0036] Embodiment 3:

[0037] Proteomic sequencing analysis of differentially expressed proteins in characteristic samples:

[0038] The peripheral serum samples collected from the children and their fathers were subjected to proteomic sequencing, and the protein sequencing 4D-DIA technology was used to analyze the differential protein expression, such as Figure 4 As shown in A, 310 differentially expressed proteins were down-regulated and 118 were up-regulated. Figure 4 B presents the expression of up- and down-regulated proteins in the form of a volcano plot.

[0039] Embodiment 4:

[0040] KEGG pathway enrichment analysis and protein expression verification:

[0041] According to the expression of differentially expressed proteins in the collected samples, the relevant pathways and key differentially expressed proteins were analyzed for proteomics verification. Figure 5 As shown in A, we selected hematopoietic-related differentially expressed proteins SERP I NG1, SERPI NB1, and PRG2 as well as MEPK pathway proteins for subsequent verification. Figure 5 As shown in B, PRG2 expression in the patient was lower than that in the father, which was consistent with the results of proteomic analysis.

[0042] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for exploring the cause of failure of A-donor O bone marrow transplantation in acute myeloid leukemia, characterized in that: The following steps are involved: Based on the GTEx and TCGA databases, combined with case samples of failed A-donor O bone marrow transplantation in acute myeloid leukemia M2, bioinformatics analysis was performed to construct a prediction model. Proteomic detection and analysis were then used to explore the biological mechanism of failure of A-donor O bone marrow transplantation in adolescent children with acute myeloid leukemia from their immediate relatives.

2. The method for investigating the cause of failure of acute myeloid leukemia A-donor O-donor bone marrow transplantation according to claim 1, characterized in that: The process of bioinformatics analysis, prediction model construction and proteomics detection and analysis specifically includes the following steps: S1. Obtain acute myeloid leukemia bone marrow samples and normal whole blood samples based on GTEx and TCGA databases, perform bioinformatics analysis, and obtain the gene expression levels in each sample; S2. Build a disease prediction model based on the analyzed data and estimate the overall survival rate; S3, differential protein analysis of experimental samples using proteomics 4D-DIA technology; S4. Verify the expression differences of important proteins and signaling pathways based on Western blotting technology.

3. The method for investigating the cause of failure of acute myeloid leukemia A-donor O-donor bone marrow transplantation according to claim 2, characterized in that: Normal whole blood samples were obtained from the GTEx database, and acute myeloid bone marrow samples and RNA transcriptome profiles of patients with acute myeloid leukemia under 18 years old were obtained from the TCGA database.

4. The method for investigating the cause of failure of acute myeloid leukemia A-donor O-donor bone marrow transplantation according to claim 2, characterized in that: In steps S3-S4, the process of detecting and analyzing the test samples specifically includes the following steps: collecting peripheral blood serum samples from a patient with acute myeloid leukemia M2 under the age of 18 and his father's donor, analyzing the differential protein expression and pathway impact between samples through 4D-DIA proteomics technology, and clarifying the different biological functional responses of the samples through differential proteins, and screening the target proteins and signal pathways based on the differential proteins for protein blotting verification.

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