Construction method of systemic lupus erythematosus evaluation model based on flow cytometry and systemic lupus erythematosus evaluation method

Abstract

The invention relates to the technical field, and discloses a construction method of a systemic lupus erythematosus evaluation model based on flow cytometry and a systemic lupus erythematosus evaluation method. A specific cell expression data training model obtained by a target group through flow cytometry is directly utilized to learn specific cell expression data of a systemic lupus erythematosus patient group, a healthy group and a disease patient group except systemic lupus erythematosus, and a high-precision systemic lupus erythematosus evaluation model is obtained. According to the present invention, the specific change of the SLE peripheral blood is focused, the expression quantities of the mononuclear cell CD169, the neutrophil CD177 and the B lymphocyte CD317 are selected, the accurate and efficient early clinical identification of the SLE is achieved through the assistance of the high-precision systemic lupus erythematosus evaluation model, and the reliable data guidance is provided for the diagnosis and treatment of the systemic lupus erythematosus patient.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to a method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry and a systemic lupus erythematosus evaluation method. Background Art

[0002] Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by immune tolerance disorders, autoantibody production, immune complex formation and deposition, leading to tissue and organ damage. SLE has diverse clinical phenotypes, with multiple and severe organ involvement, often involving multiple systems such as the kidneys, cardiovascular system, skin, central nervous system, blood system, and respiratory system.

[0003] At present, it is believed that the onset of SLE is related to factors such as genetics, environment, infection, and hormone levels. Its core pathological mechanism is immune imbalance, and type I interferon (IFN-I) and its pathway play a decisive role in SLE immune imbalance. The level of IFN-I in the blood circulation of SLE patients is significantly increased, and their peripheral blood mononuclear cells show IFN characteristic gene expression. However, there are challenges in directly measuring type I interferon. On the one hand, due to the low concentration, it is limited by the number and instability of different ligands in serum, making it difficult to obtain a good correlation with biology. In addition, although the latest single-molecule array can be accurately measured, it is expensive and has limited availability. At present, the clinical diagnosis of SLE mainly relies on symptom assessment and measurement of serum autoantibodies, which has low sensitivity and specificity and is prone to misdiagnosis.

[0004] Therefore, there is an urgent need for a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry to assist in the accurate and efficient diagnosis of systemic lupus erythematosus patients through the systemic lupus erythematosus assessment model. Summary of the invention

[0005] The present invention provides a method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry and a systemic lupus erythematosus evaluation method, so as to solve the defects of poor precision and low accuracy of current SLE diagnostic methods.

[0006] The present invention provides a method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry, comprising:

[0007] Obtaining specific cell expression data of the target population obtained by flow cytometry, wherein the target population includes an experimental group and a control group, which is a systemic lupus erythematosus patient group, and the control group includes a healthy group and a group of patients with diseases other than systemic lupus erythematosus;

[0008] According to the specific cell expression data of the target group, the specific cell expression data of the systemic lupus erythematosus patient group, the healthy group, and the patient group with diseases other than systemic lupus erythematosus are learned through the model to obtain the systemic lupus erythematosus evaluation model.

[0009] According to a method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry provided by the present invention, the specific cell expression data of a systemic lupus erythematosus patient group, a healthy group, and a patient group with diseases other than systemic lupus erythematosus are learned by a model based on the specific cell expression data of a target group, so as to obtain a systemic lupus erythematosus evaluation model, comprising:

[0010] When the specific cell expression data of the target population indicates expression data of only one specific cell, a ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group is obtained based on the specific cell expression data of the experimental group and the control group, thereby obtaining a preset individual indicator threshold value for the specific cell expression data;

[0011] When the specific cell expression data of the target population represents expression data containing more than two specific cells, the combined index value of the experimental group and the combined index value of the control group are obtained according to the specific cell expression data of the experimental group and the control group, and the ROC curve of the combined index value of the experimental group versus the combined index value of the control group is obtained, thereby obtaining the preset combined index threshold value of the specific cell expression data.

[0012] According to a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry provided by the present invention, the specific cell expression data includes any one of the following or any combination thereof: monocyte CD169 expression data, neutrophil CD177 expression data, and B lymphocyte CD317 expression data.

[0013] According to a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry provided by the present invention, the preset individual indicator threshold includes any one of the following or any combination thereof: a preset individual indicator threshold for monocyte CD169 expression data, a preset individual indicator threshold for neutrophil CD177 expression data, and a preset individual indicator threshold for B lymphocyte CD317 expression data.

[0014] The present invention provides a method for evaluating systemic lupus erythematosus, comprising:

[0015] Receiving specific cell expression data obtained by flow cytometry from a subject from at least one terminal;

[0016] According to the specific cell expression data of the subject, the systemic lupus erythematosus evaluation model obtained by any of the above-mentioned methods for constructing a systemic lupus erythematosus evaluation model based on flow cytometry is used to obtain an evaluation result of whether the subject is or is a candidate for a systemic lupus erythematosus patient;

[0017] The evaluation result of whether the subject is or is a candidate for being a systemic lupus erythematosus patient is output to at least one terminal.

[0018] According to a method for evaluating systemic lupus erythematosus provided by the present invention, the method obtains an evaluation result of whether the subject is or is a candidate for a systemic lupus erythematosus patient based on the specific cell expression data of the subject through a systemic lupus erythematosus evaluation model, including:

[0019] When the specific cell expression data of the subject to be tested indicates expression data of only one specific cell, the specific cell expression data of the subject to be tested is compared with the corresponding preset individual indicator threshold value through the systemic lupus erythematosus evaluation model. When the specific cell expression data of the subject to be tested is greater than the preset individual indicator threshold value, the subject to be tested is determined to be or is a candidate for being a systemic lupus erythematosus patient. When the specific cell expression data of the subject to be tested is less than or equal to the preset individual indicator threshold value, the subject to be tested is determined not to be or is not a candidate for being a systemic lupus erythematosus patient.

[0020] When the specific cell expression data of the subject indicates expression data of two or more specific cells, the systemic lupus erythematosus evaluation model is used to determine whether the subject is or is a candidate for a systemic lupus erythematosus patient using a joint evaluation expression.

[0021] According to a method for evaluating systemic lupus erythematosus provided by the present invention, when the specific cell expression data of the subject to be tested indicates expression data of two or more specific cells, whether the subject to be tested is or is a candidate for being a systemic lupus erythematosus patient is determined by using a systemic lupus erythematosus evaluation model and a joint evaluation expression, including:

[0022] When the specific cell expression data of the subject to be tested indicates expression data of two or more specific cells, the combined index value of the subject to be tested is obtained by using the combined evaluation expression through the systemic lupus erythematosus evaluation model;

[0023] The combined index value of the subject to be tested is compared with the preset combined index threshold. When the combined index value of the subject to be tested is greater than the preset combined index threshold, the subject to be tested is determined to be or is a candidate for a systemic lupus erythematosus patient. When the combined index value of the subject to be tested is less than or equal to the preset combined index threshold, the subject to be tested is determined not to be or is not a candidate for a systemic lupus erythematosus patient.

[0024] According to a systemic lupus erythematosus evaluation method provided by the present invention, the combined evaluation expression is:

[0025] C=Q*a+W*b+E*c-2.811

[0026] In the joint evaluation expression, C represents the joint index value of the subject, a represents the neutrophil CD177 expression data of the subject, b represents the monocyte CD169 expression data of the subject, c represents the B lymphocyte CD317 expression data of the subject, Q represents the weight of the neutrophil CD177 expression data, W represents the weight of the monocyte CD169 expression data, and E represents the weight of the B lymphocyte CD317 expression data.

[0027] According to a method for evaluating systemic lupus erythematosus provided by the present invention, the subject to be tested may be a suspected systemic lupus erythematosus patient, and may further be a suspected systemic lupus erythematosus patient with negative anti-dsDNA antibodies.

[0028] The present invention also provides a systemic lupus erythematosus evaluation system, comprising:

[0029] The data receiving module is used to: receive specific cell expression data obtained by flow cytometry from at least one terminal;

[0030] An evaluation module, used to: obtain an evaluation result of whether the subject to be tested is or is a candidate for being a systemic lupus erythematosus patient based on the specific cell expression data of the subject to be tested and the systemic lupus erythematosus evaluation model obtained by any of the above-mentioned methods for constructing a systemic lupus erythematosus evaluation model based on flow cytometry;

[0031] The data output module is used to output the evaluation result of whether the subject is or is a candidate for being a systemic lupus erythematosus patient to at least one terminal.

[0032] It should be noted that the terminal refers to an input and output device connected to a computer system. Depending on the function, the terminal can be divided into several types: smart terminal or intelligent terminal, dumb terminal, interactive terminal or online terminal. The terminal can specifically be various mobile communication devices, such as mobile phones, tablet computers, etc. The purpose of this article is to provide users with the function of inputting data and obtaining data output.

[0033] According to a systemic lupus erythematosus evaluation system provided by the present invention, the evaluation module includes:

[0034] A separate evaluation submodule is used to: when the specific cell expression data of the target population indicates expression data of only one specific cell, obtain a ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group based on the specific cell expression data of the experimental group and the control group, thereby obtaining a preset separate indicator threshold value of the specific cell expression data;

[0035] The joint evaluation submodule is used for: when the specific cell expression data of the target population indicates expression data containing two or more specific cells, obtaining the joint index value of the experimental group and the joint index value of the control group according to the specific cell expression data of the experimental group and the control group, and obtaining the ROC curve of the joint index value of the experimental group versus the joint index value of the control group, thereby obtaining the preset joint index threshold of the specific cell expression data.

[0036] According to a systemic lupus erythematosus evaluation system provided by the present invention, the joint evaluation submodule includes:

[0037] The combined index value calculation submodule is used to: when the specific cell expression data of the subject to be tested indicates that the expression data of two or more specific cells are included, the combined index value of the subject to be tested is obtained by using the combined evaluation expression through the systemic lupus erythematosus evaluation model;

[0038] The comparison submodule is used to compare the combined index value of the subject to be tested with the preset combined index threshold. When the combined index value of the subject to be tested is greater than the preset combined index threshold, it is determined that the subject to be tested is or is a candidate for a systemic lupus erythematosus patient; when the combined index value of the subject to be tested is less than or equal to the preset combined index threshold, it is determined that the subject to be tested is not or is not a candidate for a systemic lupus erythematosus patient.

[0039] The present invention also provides an electronic device, comprising a processor and a memory storing a computer program, wherein when the processor executes the computer program, it implements any of the above-mentioned methods for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or a systemic lupus erythematosus assessment method.

[0040] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or a systemic lupus erythematosus assessment method.

[0041] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute any of the above-mentioned methods for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or systemic lupus erythematosus assessment methods.

[0042] The present invention provides a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry and a systemic lupus erythematosus assessment method, which directly utilizes the specific cell expression data obtained by flow cytometry of the target population to train the model to learn the specific cell expression data of the systemic lupus erythematosus patient group, the healthy group, and the patient group with diseases other than systemic lupus erythematosus, thereby obtaining a high-precision systemic lupus erythematosus assessment model.

[0043] The present invention provides a method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry and a systemic lupus erythematosus evaluation method, which focus on the unique changes in SLE peripheral blood, select the expression levels of monocyte CD169, neutrophil CD177 and B lymphocyte CD317, and assist in achieving accurate and efficient clinical early identification of SLE through the systemic lupus erythematosus evaluation model, providing reliable data guidance for the diagnosis and treatment of patients with systemic lupus erythematosus. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic diagram of a process for constructing a systemic lupus erythematosus evaluation model provided by the present invention.

[0046] Figure 2 Gene expression in the mononuclear cell (PBMC) transcriptome and the neutrophil transcriptome is shown, where SLE represents the systemic lupus erythematosus patient population, RA represents the rheumatoid arthritis patient population, and HC represents the healthy population.

[0047] Figure 3 Gene expressions of CD169 (SIGLEC1) and CD317 (BST2) from public databases are shown.

[0048] Figure 4 The figure shows the gating strategy for detecting the expression levels of CD169 / CD177 / CD317 in peripheral blood by flow cytometry.

[0049] Figure 5 The MFI expression levels of CD169 / CD177 / CD317 and the ROC curve in Example 1 are shown.

[0050] Figure 6 The MFI expression levels of CD169 / CD177 / CD317 in the training set and validation set in Example 2 are shown.

[0051] Figure 7 The evaluation performance of the CD169 / CD177 / CD317 combination evaluation is shown.

[0052] Figure 8 The MFI expression levels of CD169 / CD177 / CD317 and the ROC curve in Example 4 are shown.

[0053] Fig. 9 This is a schematic diagram of the structure of a systemic lupus erythematosus evaluation system provided by the present invention.

[0054] Fig.10 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. 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. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0056] The present invention believes that flow cytometry is a simple, rapid, repeatable method with a small sample volume, which can quickly and accurately detect and analyze the composition, quantity, state, differentiation stage, etc. of cells, thereby making up for the limitations of simple blood routine. By characterizing the protein expression of immune cells with different phenotypes and functions in peripheral blood, important details can be provided for better diagnosis and prediction of the disease. Immune cell imbalance and dysfunction are important pathological mechanisms of SLE. Direct detection of immune cell surface protein and functional protein expression by flow cytometry in clinical practice can assist in the accurate diagnosis and treatment of SLE in clinical practice.

[0057] Figure 1 A schematic flow chart of a method for constructing a systemic lupus erythematosus evaluation model provided by the present invention. The execution subject of the method for constructing a systemic lupus erythematosus evaluation model provided by the present invention can be any applicable terminal side device or network side device, such as a construction device for a systemic lupus erythematosus evaluation model.

[0058] See also Figure 1 The method for constructing a systemic lupus erythematosus evaluation model provided by the present invention may include:

[0059] S110, obtaining specific cell expression data of the target population obtained by flow cytometry, wherein the target population includes an experimental group and a control group, which is a systemic lupus erythematosus patient group, and the control group includes a healthy group and a disease patient group other than systemic lupus erythematosus, wherein the specific cell expression data may include any one of the following items or any combination thereof: monocyte CD169 expression data, neutrophil CD177 expression data, B lymphocyte CD317 expression data;

[0060] S120. Based on the specific cell expression data of the target group, the specific cell expression data of the systemic lupus erythematosus patient group, the healthy group, and the patient group with diseases other than systemic lupus erythematosus are learned through the model to obtain a systemic lupus erythematosus evaluation model.

[0061] In one embodiment, S120 may include:

[0062] When the specific cell expression data of the target population represents expression data of only one specific cell, a ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group is obtained according to the specific cell expression data of the experimental group and the control group, thereby obtaining a preset individual indicator threshold value of the specific cell expression data, wherein the preset individual indicator threshold value may include any one of the following or any combination thereof: a preset individual indicator threshold value of monocyte CD169 expression data, a preset individual indicator threshold value of neutrophil CD177 expression data, and a preset individual indicator threshold value of B lymphocyte CD317 expression data;

[0063] When the specific cell expression data of the target population represents expression data containing more than two specific cells, the combined index value of the experimental group and the combined index value of the control group are obtained according to the specific cell expression data of the experimental group and the control group, and the ROC curve of the combined index value of the experimental group versus the combined index value of the control group is obtained, thereby obtaining the preset combined index threshold value of the specific cell expression data.

[0064] After obtaining the systemic lupus erythematosus assessment model, it can be applied to systemic lupus erythematosus assessment to form a systemic lupus erythematosus assessment method. It should be noted that the implementation form of the systemic lupus erythematosus assessment method provided by the present invention can be realized by programming software, and its execution subject can be any applicable terminal side device or network side device, such as a systemic lupus erythematosus assessment device, etc.

[0065] The present invention provides a method for evaluating systemic lupus erythematosus, which may include:

[0066] S210, receiving specific cell expression data obtained by flow cytometry from a subject from at least one terminal;

[0067] S220, obtaining an evaluation result of whether the subject is or is a candidate for being a systemic lupus erythematosus patient by using a systemic lupus erythematosus evaluation model according to the specific cell expression data of the subject;

[0068] S230: Outputting the evaluation result of whether the subject is or is a candidate for being a systemic lupus erythematosus patient to at least one terminal.

[0069] In one embodiment, S220 may include:

[0070] When the specific cell expression data of the subject to be tested indicates expression data of only one specific cell, the specific cell expression data of the subject to be tested is compared with the corresponding preset individual indicator threshold value through the systemic lupus erythematosus evaluation model. When the specific cell expression data of the subject to be tested is greater than the preset individual indicator threshold value, the subject to be tested is determined to be or is a candidate for being a systemic lupus erythematosus patient. When the specific cell expression data of the subject to be tested is less than or equal to the preset individual indicator threshold value, the subject to be tested is determined not to be or is not a candidate for being a systemic lupus erythematosus patient.

[0071] When the specific cell expression data of the subject to be tested indicates expression data of two or more specific cells, the combined index value of the subject to be tested is obtained by using the combined evaluation expression through the systemic lupus erythematosus evaluation model;

[0072] The combined index value of the subject to be tested is compared with the preset combined index threshold. When the combined index value of the subject to be tested is greater than the preset combined index threshold, the subject to be tested is determined to be or is a candidate for a systemic lupus erythematosus patient. When the combined index value of the subject to be tested is less than or equal to the preset combined index threshold, the subject to be tested is determined not to be or is not a candidate for a systemic lupus erythematosus patient.

[0073] Among them, the joint evaluation expression is:

[0074] C=Q*a+W*b+E*c-2.811

[0075] In the joint evaluation expression, C represents the joint index value of the subject, a represents the neutrophil CD177 expression data of the subject, b represents the monocyte CD169 expression data of the subject, c represents the B lymphocyte CD317 expression data of the subject, Q represents the weight of the neutrophil CD177 expression data, W represents the weight of the monocyte CD169 expression data, and E represents the weight of the B lymphocyte CD317 expression data.

[0076] The following will use multiple examples to verify the method for constructing a systemic lupus erythematosus assessment model and the systemic lupus erythematosus assessment method provided by the present invention.

[0077] The characteristics of the case samples used in the following examples are shown in Table 1, which is a basic characteristic table of the disease group (systemic lupus erythematosus group), healthy control group and disease control group (patients suffering from diseases other than systemic lupus erythematosus) included in the examples.

[0078] Table 1 Basic characteristics of systemic lupus erythematosus, disease controls and healthy controls

[0079]

[0080] Note: The systemic lupus erythematosus group refers to patients who were clinically diagnosed according to the diagnostic classification criteria for systemic lupus erythematosus (SLE) jointly issued by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR) in 2019; the healthy control group (HC) included healthy volunteers with normal indicators of the physical examination center; the disease control group (DC) included patients who were clearly diagnosed according to the diagnostic criteria for various diseases, including 26 cases of rheumatoid arthritis, 14 cases of Sjögren's syndrome, 6 cases of adult Still's disease, 5 cases of systemic vasculitis, 3 cases of multiple sclerosis, and 2 cases of connective tissue disease.

[0081] Example 1

[0082] 1. Discovery of CD169 / CD177 / CD317 on the surface of immune cells as a marker for the diagnosis of systemic lupus erythematosus based on flow cytometry

[0083] Example The peripheral blood mononuclear cell transcriptome and neutrophil transcriptome of 14 patients with systemic lupus erythematosus, 15 patients with rheumatoid arthritis and 13 healthy subjects were analyzed in the early stage (the population information is shown in Table 2).

[0084] Table 2 Basic characteristics of systemic lupus erythematosus, rheumatoid arthritis and healthy controls

[0085]

[0086] *p value compares the difference between SLE and healthy controls

[0087] #p value comparing the differences between SLE and RA patient groups

[0088] By differential analysis, the gene expression of CD169 (SIGLEC1) and CD317 (BST2) was significantly upregulated in the transcriptome of peripheral blood mononuclear cells (PBMCs) of patients with systemic lupus erythematosus ( Figure 2 A, 2B), CD177 gene expression was significantly upregulated in the neutrophil transcriptome ( Figure 2 C), suggesting that immune cell markers have diagnostic value for systemic lupus erythematosus.

[0089] To verify the diagnostic value of immune cell markers for systemic lupus erythematosus, the transcriptome data of systemic lupus erythematosus immune cell subsets were downloaded from a public database (GSE148601). The example found that the expression of CD169 (SIGLEC1) in the mononuclear cell transcriptome of patients with systemic lupus erythematosus was significantly upregulated and significantly downregulated after clinical treatment ( Figure 3 A); The expression of CD317 (BST2) was significantly upregulated in the transcriptome of memory B cells and naive B cells ( Figure 3 B), revealing that the expression of CD169 on the surface of monocytes and CD317 on the surface of B cells have diagnostic value for systemic lupus erythematosus.

[0090] From the above, it can be seen that the expression of CD169 molecules on the surface of monocytes, CD317 molecules on the surface of B cells and CD177 molecules on the surface of neutrophils in patients with systemic lupus erythematosus is significantly different between healthy people, disease control groups and patients with systemic lupus erythematosus, suggesting that it has clinical application value for the diagnosis of systemic lupus erythematosus.

[0091] 2. Establishment of a method for diagnosing systemic lupus erythematosus based on flow cytometry detection of immune cell markers 1. Use the whole peripheral blood of the test subject, lyse and remove the red blood cells, and then perform flow staining.

[0092] The details are as follows:

[0093] 1) Collect 400 μL of peripheral EDTA anticoagulated whole blood from subjects (including clinically confirmed SLE patients, healthy controls and disease controls) and pipette it into a 15 mL clean centrifuge tube;

[0094] 2) Add 8 ml of 1X red blood cell lysis buffer (BD Biosciences, 10X, diluted in ultrapure water) pre-equilibrated to room temperature per 2 ml of lysis buffer / 100 μl of whole blood, tighten the lid, gently invert and mix for 1 minute, and let stand at room temperature for 10 minutes to lyse the red blood cells;

[0095] 3) Fill the tube with PBS (Gibco), mix well, centrifuge at 1500 rpm (fast rise and fall) at room temperature for 5 minutes, and discard the supernatant;

[0096] 4) Add 1 ml of PBS and blow evenly, then fill up with PBS, mix well and centrifuge under the above conditions;

[0097] 5) After centrifugation, discard the supernatant, add 200 μl PBS to resuspend and transfer to a 96-well plate, centrifuge (same conditions as above), quickly shake the plate once and discard the supernatant;

[0098] 6) Add 100 μL / well of Fc blocking solution (Biolegend, diluted 1:50 in PBS), blow evenly, and block on ice at 4°C for 10 minutes;

[0099] 7) Add 100 μl PBS and centrifuge as above;

[0100] 8) Shake the plate and discard the supernatant, add 100 μL / well of flow cytometry antibody, mix well, and incubate at 4°C in the dark for 30 minutes. The antibodies are prepared as follows: APC / Cy7-labeled anti-CD19 antibody (Biolegend, 1:100), PerCP-Cyanine5.5-labeled anti-CD15 antibody (Biolegend, 1:100), PE-labeled anti-CD14 antibody (Biolegend, 1:100), PE-Cyanine7-labeled anti-CD317 antibody or PE-Cyanine7-labeled anti-mouse IgG1, κ chain antibody (Biolegend, 1:50), FITC-labeled anti-CD177 antibody or FITC-labeled anti-mouse IgG1, κ chain antibody (Biolegend, 1:50), APC-labeled anti-CD169 antibody or APC-labeled anti-mouse IgG1, κ chain antibody (Biolegend, 1:50), all antibodies are diluted in PBS.

[0101] 9) After staining, add 100 μl of PBS to each well and centrifuge under the above conditions;

[0102] 10) Shake the plate and discard the supernatant, add 200 μl PBS to each well, and centrifuge again;

[0103] 11) Shake the plate and discard the supernatant, add 200 μl of 2% PFA, blow evenly and transfer to the marked flow tube.

[0104] 2. Detection of CD169 / CD177 / CD317 expression

[0105] The cells were analyzed using FACSCantoTMⅡ (BD Biosciences) flow cytometer. Figure 4 The gating strategy shown in the figure separates monocytes (CD14+), B cells (CD19+), and neutrophils (CD15+). The median fluorescence intensity (MFI) values ​​of CD169 on monocytes, CD177 on neutrophils, CD317 on B lymphocytes, and the expression of their respective isotype control antibodies were recorded using FlowJo software (BD Biosciences). The expression intensity of the surface markers of sample immune cells is defined as the MFI value of the molecule to be tested minus the MFI value of the isotype antibody of the corresponding channel. The gating strategy is as follows Figure 4 shown.

[0106] 3. Determination of threshold and judgment criteria

[0107] The subjects shown in Table 1 were used as test samples, including 100 patients with clinically confirmed systemic lupus erythematosus (also referred to as test samples) and 116 control group patients (including 60 healthy controls and 56 disease controls).

[0108] All samples were randomly divided into training set and validation set in a ratio of 7:3. The training set included 72 patients with SLE and 79 control patients (38 healthy patients and 41 disease controls). The validation set included 28 patients with SLE and 37 control patients (22 healthy patients and 15 disease controls).

[0109] The threshold was determined based on the CD169 / CD177 / CD317 MFI expression of each sample in the training set detected by flow cytometry. The threshold was determined based on the CD169 / CD177 / CD317 MFI expression of the systemic lupus erythematosus group in the training set compared with the control group ( Figure 5 A, 5B, 5C) to draw the ROC curve and take the critical value at the maximum value of Youden index. Figure 5 D, 5E, 5F), it can be seen that the threshold of CD169 expression is 501.6; the threshold of CD177 expression is 6651; the threshold of CD317 expression is 874.5. If the CD169 / CD177 / CD317 expression of the subject is greater than the corresponding threshold, the subject is determined to be or is a candidate for a systemic lupus erythematosus patient; if the CD169 / CD177 / CD317 expression of the subject is less than or equal to the threshold, the subject is determined not to be or is not a candidate for a systemic lupus erythematosus patient.

[0110] Example 2: Application in diagnosis of systemic lupus erythematosus

[0111] 1. Detection

[0112] The subjects shown in Table 1 were used as test samples, including 100 patients with clinically confirmed systemic lupus erythematosus (also referred to as test samples) and 116 control group patients (including 60 healthy controls and 56 disease controls).

[0113] All samples were randomly divided into training set and validation set in a ratio of 7:3. The training set included 72 patients with SLE and 79 control patients (38 healthy patients and 41 disease controls). The validation set included 28 patients with SLE and 37 control patients (22 healthy patients and 15 disease controls).

[0114] The detection was performed according to the method of Example 1.

[0115] The training set and validation set results of the samples shown in Table 1 are as follows Figure 6As shown, it can be seen that there are significant differences in the expression levels of CD169 / CD177 / CD317 in the systemic lupus erythematosus group and the control group, further confirming that these three molecular proteins can be used as diagnostic markers for patients with systemic lupus erythematosus.

[0116] 2. Sensitivity and specificity testing

[0117] The CD169 / CD177 / CD317 expression levels of each systemic lupus erythematosus patient and the control group in the training set were used to draw ROC curves, and the critical value at the maximum Youden index was taken as the threshold. The training set and validation set samples were used for diagnostic prediction, and their sensitivity, specificity, positive predictive value, and negative predictive value were calculated. The results are shown in Tables 3, 4, and 5, which list the diagnostic sensitivity, specificity, positive predictive value, and negative predictive value, respectively. It can be seen that the single CD169 / CD177 / CD317 expression level can be used as a marker to assist in the diagnosis of systemic lupus erythematosus patients, and the sensitivity and (or) specificity are high.

[0118] Table 3 Sensitivity, specificity, positive predictive value and negative predictive value of CD169

[0119]

[0120] Table 4 Sensitivity, specificity, positive predictive value and negative predictive value of CD177

[0121]

[0122] Table 5 Sensitivity, specificity, positive predictive value and negative predictive value of CD317

[0123]

[0124] Example 3: Combined diagnosis of systemic lupus erythematosus

[0125] 1. Joint Index Evaluation Formula

[0126] It can be seen from the above Examples 1 and 2 that the expression of CD169, CD177, and CD317 alone can be used to assist in the diagnosis of patients with systemic lupus erythematosus. In order to further study whether the combined diagnosis of the three can improve the diagnostic efficiency, this example constructs a combined indicator evaluation formula based on the results of the training set:

[0127] C=1.959*a+2.328*b+2.194*c-2.811

[0128] Where C is the combined index value, a is the MFI expression of CD177, b is the MFI expression of CD169, and c is the MFI expression of CD317.

[0129] 2. Establishment of a method for combined diagnosis of systemic lupus erythematosus using immune cell surface markers

[0130] 1. Same as 1 in Example 1;

[0131] 2. Same as 2 in Example 1;

[0132] 3. Substitute the target molecule CD169 / CD177 / CD317 MFI expression level obtained in 2 above into the following combined index evaluation formula to calculate the combined index value.

[0133] According to the ROC curve of the combined index value of the systemic lupus erythematosus group versus the control group in the training set and the ROC curve of the single index ( Figure 8 ), take the critical value of the Youden index maximum value of the ROC curve of the combined index value, and obtain the combined index value threshold of 0.4965.

[0134] If the combined index value of the subject to be tested is greater than the combined index value threshold, the subject to be tested is determined to be or is a candidate for a systemic lupus erythematosus patient. If the combined index value of the subject to be tested is less than or equal to the combined index value threshold, the subject to be tested is determined not to be or is not a candidate for a systemic lupus erythematosus patient.

[0135] 3. Application of immune cell surface markers in the combined diagnosis of systemic lupus erythematosus

[0136] The subjects shown in Table 1 were used as test samples, including 100 patients with clinically confirmed systemic lupus erythematosus (also referred to as test samples) and 116 control group patients (including 60 healthy controls and 56 disease controls).

[0137] All samples were randomly divided into training set and validation set in a ratio of 7:3. The training set included 72 patients with SLE and 79 control patients (38 healthy patients and 41 disease controls). The validation set included 28 patients with SLE and 37 control patients (22 healthy patients and 15 disease controls).

[0138] The CD169 / CD177 / CD317 expression values ​​of each SLE patient and the control group in the training set and the validation set were substituted into the above-mentioned combined index evaluation formula to calculate the combined index value of each subject.

[0139] The combined index values ​​of each SLE patient and the control group in the training set were plotted into an ROC curve, and the critical value at the maximum Youden index was taken as the threshold value ( Figure 5 D, 5E, 5F), make diagnostic predictions for the training set and validation set samples respectively, and calculate their respective sensitivity, specificity, positive predictive value, and negative predictive value.

[0140] The results are shown in Table 6. It can be seen that the combined CD169 / CD177 / CD317 MFI expression can be used to assist in the diagnosis of patients with systemic lupus erythematosus with high sensitivity and specificity.

[0141] Table 6 Sensitivity, specificity, positive predictive value and negative predictive value of combined diagnostic C value

[0142]

[0143] Example 4: CD169 / CD177 / CD317 alone and in combination as auxiliary indicators for diagnosis of anti-dsDNA negative systemic lupus erythematosus

[0144] 1. Detection

[0145] The subjects shown in Table 1 were used as test samples, 100 patients with clinically confirmed systemic lupus erythematosus (also referred to as test samples) and 116 control group patients (including 60 healthy controls and 56 disease controls). It is worth noting that anti-dsDNA, as a classic diagnostic marker for systemic lupus erythematosus, has a clinical problem of low diagnostic sensitivity. Among the patient population included in the study in this example, 36 were negative for anti-dsDNA antibodies, and their basic information is shown in Table 7.

[0146] The anti-dsDNA antibody negative samples and the control group were randomly divided into a training set and a validation set at a ratio of 7:3. The training set included 25 anti-dsDNA antibody negative SLE patients and 79 control patients (38 healthy subjects and 41 disease controls), and the validation set included 11 anti-dsDNA antibody negative SLE patients and 37 control patients (22 healthy subjects and 15 disease controls), as shown in Table 7.

[0147] Table 7 Basic characteristics of anti-dsDNA negative systemic lupus erythematosus, disease controls and healthy controls

[0148]

[0149] The detection was performed according to the method of Example 1.

[0150] The training set and validation set results of the samples shown in Table 7 are as follows Figure 8 As shown, it can be seen that there are significant differences in the expression levels of CD169 / CD177 / CD317 in the anti-dsDNA antibody-negative systemic lupus erythematosus group and the control group, further confirming that these three molecular proteins can be used as diagnostic markers for anti-dsDNA antibody-negative systemic lupus erythematosus patients.

[0151] 2. Sensitivity and specificity of CD169 / CD177 / CD317 detection

[0152] The CD169 / CD177 / CD317 expression levels of each anti-dsDNA antibody-negative systemic lupus erythematosus patient in the training set and the control group were used to draw ROC curves, and the critical value at the maximum Youden index was taken as the threshold. The training set and validation set samples were used for diagnostic prediction, and their sensitivity, specificity, positive predictive value, and negative predictive value were calculated. The results are shown in Tables 8, 9, and 10, which list the diagnostic sensitivity, specificity, positive predictive value, and negative predictive value, respectively. It can be seen that the CD169 / CD177 / CD317 expression level alone can be used as a marker to assist in the diagnosis of anti-dsDNA antibody-negative systemic lupus erythematosus patients, while ensuring good diagnostic specificity and greatly improving the diagnostic sensitivity.

[0153] Table 8 Sensitivity, specificity, positive predictive value and negative predictive value of CD169 for the diagnosis of anti-dsDNA-SLE

[0154]

[0155] Table 9 Sensitivity, specificity, positive predictive value and negative predictive value of CD177 for the diagnosis of anti-dsDNA-SLE

[0156]

[0157]

[0158] Table 10 Sensitivity, specificity, positive predictive value and negative predictive value of CD317 for anti-dsDNA-SLE diagnosis

[0159]

[0160] 3. Sensitivity and specificity detection of CD169 / CD177 / CD317 combined diagnostic model

[0161] It can be seen from the above Example 3 that the combined index evaluation model constructed based on the results of the overall systemic lupus erythematosus patient training set has good diagnostic performance and can be used to assist in the diagnosis of systemic lupus erythematosus patients. The next step is to further study whether the three-in-one combined diagnostic model can improve the diagnostic efficiency in anti-dsDNA antibody-negative systemic lupus erythematosus.

[0162] The combined index evaluation formula is C = 1.959*a+2.328*b+2.194*c-2.811

[0163] Where C is the combined index C value, a is the MFI expression of CD177, b is the MFI expression of CD169, and c is the MFI expression of CD317. The critical value at the maximum value of the Youden index of the combined diagnosis ROC curve is taken, and the combined index value threshold is 0.4965. If the combined index value of the subject to be tested is greater than the combined index value threshold, the subject to be tested is judged to be or is a candidate for systemic lupus erythematosus. If the combined index value of the subject to be tested is less than or equal to the combined index value threshold, the subject to be tested is judged not to be or is not a candidate for systemic lupus erythematosus.

[0164] The ROC curve and diagnostic performance of the combined indicator evaluation model for the diagnosis of systemic lupus erythematosus group with negative anti-dsDNA antibodies in the training set are shown in Figure 2. Fig. 9 shown.

[0165] The above examples used flow cytometry to detect CD169 / CD177 / CD317, and the results showed that the systemic lupus erythematosus group was significantly upregulated compared with the control group (eg Figure 5 A, as shown). ROC curve was drawn based on the expression of CD169 / CD177 / CD317 in patients with systemic lupus erythematosus and the control group (as shown in Figure 5 The critical value at the maximum Youden index was taken as the threshold, and diagnostic predictions were made for the training set and validation set samples respectively. The sensitivity, specificity, positive predictive value and negative predictive value of each and the combination of the three were calculated. The diagnostic sensitivity of the CD169 / CD177 / CD317 joint evaluation model was 82.76%, and the specificity was 87.80% (as shown in Table 11). Compared with the traditional classic anti-dsDNA, the sensitivity of disease diagnosis was greatly improved while ensuring the specificity, which can make up for the high clinical missed diagnosis rate to a certain extent and contribute to the differential diagnosis of systemic lupus erythematosus.

[0166] Table 11 Diagnostic performance of various markers in diagnosing systemic lupus erythematosus

[0167]

[0168] The present invention provides the application of a substance for detecting the expression of CD169 / CD177 / CD317MFI in the preparation of a product for auxiliary diagnosis of systemic lupus erythematosus. Also provided is the application of a substance for detecting the expression of CD169 / CD177 / CD317MFI by flow cytometry and a carrier loaded with the following combined index C value model formula in the preparation of a product for auxiliary diagnosis of systemic lupus erythematosus; the present invention focuses on the unique changes of a specific group of peripheral blood of SLE, selects the expression of a specific expression marker CD169 (CD14+MoCD169) on CD14-positive monocytes, a specific expression marker CD177 (CD15+NECD177) on CD15-positive neutrophils, and a specific expression marker CD317 (CD19+BCD317) in CD19-positive B lymphocytes, assists in the clinical differential diagnosis of SLE, and constructs a model for combined diagnosis of CD169 / CD177 / CD317, realizing the diagnosis or screening of SLE; it has high sensitivity and specificity.

[0169] The present invention provides a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry and a systemic lupus erythematosus assessment method, which directly utilizes the specific cell expression data obtained by flow cytometry of the target population to train the model to learn the specific cell expression data of the systemic lupus erythematosus patient group, the healthy group, and the patient group with diseases other than systemic lupus erythematosus, thereby obtaining a high-precision systemic lupus erythematosus assessment model.

[0170] The present invention provides a method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry and a systemic lupus erythematosus evaluation method, which focus on the unique changes in SLE peripheral blood, select the expression levels of monocyte CD169, neutrophil CD177 and B lymphocyte CD317, and assist in achieving accurate and efficient clinical early identification of SLE through the systemic lupus erythematosus evaluation model, providing reliable data guidance for the diagnosis and treatment of patients with systemic lupus erythematosus.

[0171] The systemic lupus erythematosus evaluation system provided by the present invention is described below. The systemic lupus erythematosus evaluation system described below and the systemic lupus erythematosus evaluation method described above can be referenced to each other.

[0172] See also Fig. 9 The present invention provides a systemic lupus erythematosus assessment system, which may include:

[0173] The data receiving module is used to: receive specific cell expression data obtained by flow cytometry from at least one terminal;

[0174] An evaluation module, used to: obtain an evaluation result of whether the subject to be tested is or is a candidate for being a systemic lupus erythematosus patient based on the specific cell expression data of the subject to be tested and the systemic lupus erythematosus evaluation model obtained by any of the above-mentioned methods for constructing a systemic lupus erythematosus evaluation model based on flow cytometry;

[0175] The data output module is used to output the evaluation result of whether the subject is or is a candidate for being a systemic lupus erythematosus patient to at least one terminal.

[0176] According to a systemic lupus erythematosus evaluation system provided by the present invention, the evaluation module includes:

[0177] A separate evaluation submodule is used to: when the specific cell expression data of the target population indicates expression data of only one specific cell, obtain a ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group based on the specific cell expression data of the experimental group and the control group, thereby obtaining a preset separate indicator threshold value of the specific cell expression data;

[0178] The joint evaluation submodule is used for: when the specific cell expression data of the target population indicates expression data containing two or more specific cells, obtaining the joint index value of the experimental group and the joint index value of the control group according to the specific cell expression data of the experimental group and the control group, and obtaining the ROC curve of the joint index value of the experimental group versus the joint index value of the control group, thereby obtaining the preset joint index threshold of the specific cell expression data.

[0179] According to a systemic lupus erythematosus evaluation system provided by the present invention, the joint evaluation submodule includes:

[0180] The combined index value calculation submodule is used to: when the specific cell expression data of the subject to be tested indicates that the expression data of two or more specific cells are included, the combined index value of the subject to be tested is obtained by using the combined evaluation expression through the systemic lupus erythematosus evaluation model;

[0181] The comparison submodule is used to compare the combined index value of the subject to be tested with the preset combined index threshold. When the combined index value of the subject to be tested is greater than the preset combined index threshold, it is determined that the subject to be tested is or is a candidate for a systemic lupus erythematosus patient; when the combined index value of the subject to be tested is less than or equal to the preset combined index threshold, it is determined that the subject to be tested is not or is not a candidate for a systemic lupus erythematosus patient.

[0182] Fig.10 An example of a physical structure diagram of an electronic device is shown in FIG. Fig.10As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute any of the above-mentioned methods for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or the systemic lupus erythematosus assessment method.

[0183] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0184] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute any of the above-mentioned methods for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or the systemic lupus erythematosus assessment method.

[0185] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute any of the above-mentioned methods for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or the systemic lupus erythematosus assessment method.

[0186] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0187] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0188] 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. A method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry, characterized in that: include: Obtaining specific cell expression data of the target population obtained by flow cytometry, wherein the target population includes an experimental group and a control group, which is a systemic lupus erythematosus patient group, and the control group includes a healthy group and a group of patients with diseases other than systemic lupus erythematosus; According to the specific cell expression data of the target group, the specific cell expression data of the systemic lupus erythematosus patient group, the healthy group, and the patient group with diseases other than systemic lupus erythematosus are learned through the model to obtain the systemic lupus erythematosus evaluation model.

2. The method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry according to claim 1, characterized in that: The specific cell expression data of the target group is used to learn the specific cell expression data of the systemic lupus erythematosus patient group, the healthy group, and the patient group with diseases other than systemic lupus erythematosus through the model to obtain the systemic lupus erythematosus evaluation model, including: When the specific cell expression data of the target population indicates expression data of only one specific cell, a ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group is obtained based on the specific cell expression data of the experimental group and the control group, thereby obtaining a preset individual indicator threshold value for the specific cell expression data; When the specific cell expression data of the target population represents expression data containing more than two specific cells, the combined index value of the experimental group and the combined index value of the control group are obtained according to the specific cell expression data of the experimental group and the control group, and the ROC curve of the combined index value of the experimental group versus the combined index value of the control group is obtained, thereby obtaining the preset combined index threshold value of the specific cell expression data.

3. The method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry according to claim 1, characterized in that: The specific cell expression data includes any one of the following or any combination thereof: monocyte CD169 expression data, neutrophil CD177 expression data, and B lymphocyte CD317 expression data.

4. The method for constructing a systemic lupus erythematosus assessment model based on flow cytometry according to claim 2, characterized in that: The preset individual indicator threshold value includes any one of the following items or any combination thereof: a preset individual indicator threshold value for monocyte CD169 expression data, a preset individual indicator threshold value for neutrophil CD177 expression data, and a preset individual indicator threshold value for B lymphocyte CD317 expression data.

5. A method for evaluating systemic lupus erythematosus, characterized in that: include: Receiving specific cell expression data obtained by flow cytometry from a subject from at least one terminal; According to the specific cell expression data of the subject, the systemic lupus erythematosus evaluation model is obtained by the method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry according to any one of claims 1 to 4, to obtain an evaluation result of whether the subject is or is a candidate for a systemic lupus erythematosus patient; The evaluation result of whether the subject is or is a candidate for being a systemic lupus erythematosus patient is output to at least one terminal.

6. The method for evaluating systemic lupus erythematosus according to claim 1, characterized in that: The systemic lupus erythematosus evaluation model obtained according to the method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry according to any one of claims 1 to 4 based on the specific cell expression data of the subject, and the evaluation result of whether the subject is or is a candidate for a systemic lupus erythematosus patient, includes: When the specific cell expression data of the target population indicates expression data of only one specific cell, a ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group is obtained based on the specific cell expression data of the experimental group and the control group, thereby obtaining a preset individual indicator threshold value for the specific cell expression data; When the specific cell expression data of the target population represents expression data containing more than two specific cells, the combined index value of the experimental group and the combined index value of the control group are obtained according to the specific cell expression data of the experimental group and the control group, and the ROC curve of the combined index value of the experimental group versus the combined index value of the control group is obtained, thereby obtaining the preset combined index threshold value of the specific cell expression data.

7. The method for evaluating systemic lupus erythematosus according to claim 1, characterized in that: When the specific cell expression data of the target population indicates expression data of two or more specific cells, the combined index value of the experimental group and the combined index value of the control group are obtained according to the specific cell expression data of the experimental group and the control group, and the ROC curve of the combined index value of the experimental group to the combined index value of the control group is obtained, so as to obtain the preset combined index threshold of the specific cell expression data, including: When the specific cell expression data of the subject to be tested indicates expression data of two or more specific cells, the combined index value of the subject to be tested is obtained by using the combined evaluation expression through the systemic lupus erythematosus evaluation model; The combined index value of the subject to be tested is compared with the preset combined index threshold. When the combined index value of the subject to be tested is greater than the preset combined index threshold, the subject to be tested is determined to be or is a candidate for a systemic lupus erythematosus patient. When the combined index value of the subject to be tested is less than or equal to the preset combined index threshold, the subject to be tested is determined not to be or is not a candidate for a systemic lupus erythematosus patient.

8. The method for evaluating systemic lupus erythematosus according to claim 1, characterized in that: The joint evaluation expression is: C=Q*a+W*b+E*c-2.811 In the joint evaluation expression, C represents the joint index value of the subject, a represents the neutrophil CD177 expression data of the subject, b represents the monocyte CD169 expression data of the subject, c represents the B lymphocyte CD317 expression data of the subject, Q represents the weight of the neutrophil CD177 expression data, W represents the weight of the monocyte CD169 expression data, and E represents the weight of the B lymphocyte CD317 expression data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for constructing a systemic lupus erythematosus assessment model based on flow cytometry as described in any one of claims 1 to 4 and / or the systemic lupus erythematosus assessment method as described in any one of claims 5 to 8 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for constructing a systemic lupus erythematosus assessment model based on flow cytometry as described in any one of claims 1 to 4 and / or the systemic lupus erythematosus assessment method as described in any one of claims 5 to 8.

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