Biomarker for evaluating progress of systemic lupus erythematosus to lupus nephritis, prediction model and application

Abstract

The invention discloses a biomarker for evaluating the progress of systemic lupus erythematosus to lupus nephritis, a prediction model and application, and belongs to the technical field of lupus nephritis prediction. The invention provides a prediction model for evaluating the progress of systemic lupus erythematosus to lupus nephritis, the prediction model performs binary Logistic regression analysis according to the ratio of biomarkers CD8 / CD4 and the ratio of kappa / lambda of young B cells to obtain a LogitP value, and the progress of systemic lupus erythematosus to lupus nephritis is evaluated by comparing the LogitP value with a critical value 0.66938. And predicting the progression of systemic lupus erythematosus to lupus nephritis. The prediction model constructed by the invention aims to identify individuals possibly developing into lupus nephritis in lupus patients. The innovative model not only provides important guiding significance in the aspects of disease diagnosis, prognosis prediction and curative effect evaluation, but also provides a new view angle for clinical management of lupus nephritis.

Description

Technical Field

[0001] The present invention relates to the technical field of lupus nephritis prediction, and particularly to a biomarker, a prediction model and an application for evaluating the progression of systemic lupus erythematosus to lupus nephritis. Background Art

[0002] Systemic lupus erythematosus (SLE) is a chronic autoimmune disease mainly occurring in women of childbearing age, with diverse and variable clinical manifestations. Lupus nephritis (LN) is the most common target organ damage in SLE. It is reported that about 60% of adult SLE patients will show symptoms of kidney involvement during their disease course, and some LN patients may be asymptomatic or have latent symptoms for a period of time, which not only seriously affects the quality of life of patients, but also significantly increases the risk of death. Therefore, in view of the high risk of the progression of SLE to LN, establishing an effective method to evaluate the possibility of LN progression in SLE patients is of crucial significance for early identification and diagnosis of LN and is crucial for guiding clinical treatment decisions. Summary of the Invention

[0003] The purpose of the present invention is to provide a biomarker, a prediction model and an application for evaluating the progression of systemic lupus erythematosus to lupus nephritis to solve the problems existing in the above prior art. The prediction model disclosed by the present invention can be used to predict the risk of LN progression in SLE patients, which is of great significance for improving the prognosis of SLE patients and guiding individualized treatment.

[0004] To solve the above problems, the present invention provides the following solutions:

[0005] Technical Solution 1: A biomarker for screening and / or diagnosing the progression of systemic lupus erythematosus to lupus nephritis, wherein the biomarker includes the CD8 / CD4 ratio and the naive B cell κ / λ ratio.

[0006] Technical Solution 2: A product for predicting the progression of systemic lupus erythematosus to lupus nephritis, including a reagent for detecting the expression level of the biomarker.

[0007] Further, the product includes a kit, a test strip or an instrument platform.

[0008] Technical Solution 3: Application of a reagent for detecting the protein expression level of the biomarker in the preparation of a kit for screening and / or diagnosing the progression of systemic lupus erythematosus to lupus nephritis.

[0009] Technical solution four: A prediction model for evaluating the progression of systemic lupus erythematosus to lupus nephritis. The prediction model performs binary Logistic regression analysis based on the expression levels of the biomarkers to obtain the LogitP value, and predicts the progression of systemic lupus erythematosus to lupus nephritis according to the LogitP value. Among them, the calculation formula of the LogitP value is: Logitp = -5.598 + 2.545*(CD8 / CD4 ratio) + 2.003*(naive B cell κ / λ ratio).

[0010] Further, when the LogitP value is higher than the critical value of 0.66938, the risk of systemic lupus erythematosus progressing to lupus nephritis is higher; when the LogitP value is lower than the critical value of 0.66938, the risk of systemic lupus erythematosus progressing to lupus nephritis is lower.

[0011] Technical solution five: A non-invasive kit for screening and / or diagnosing the progression of systemic lupus erythematosus to lupus nephritis. The kit performs binary Logistic regression analysis based on the CD8 / CD4 ratio and the naive B cell κ / λ ratio in the peripheral blood sample of the tested person to obtain the LogitP value. When the LogitP value is higher than the critical value of 0.66938, the risk of the tested person progressing to lupus nephritis is higher; when the LogitP value is lower than the critical value of 0.66938, the risk of the tested person progressing to lupus nephritis is lower.

[0012] Further, the calculation formula of the LogitP value is: Logitp = -5.598 + 2.545*(CD8 / CD4 ratio) + 2.003*(naive B cell κ / λ ratio).

[0013] The present invention discloses the following technical effects:

[0014] The present invention has developed a non-invasive prediction model for predicting the risk of SLE patients progressing to LN, which is of great significance for improving the prognosis of SLE patients and guiding individualized treatment. By integrating potential biomarkers in the patient's circulation, this model aims to provide a prediction tool to help clinicians more effectively manage SLE patients, especially those at high risk of developing LN. The present invention comprehensively compared the circulating immune cells of systemic lupus erythematosus patients without kidney damage (SLE-NKI) and LN, revealing the unique circulating immune cell characteristics of LN patients. On this basis, the present invention constructed a non-invasive clinical prediction model to identify individuals among lupus patients who may develop into LN. This innovative model not only provides important guiding significance in the diagnosis, prognosis prediction and efficacy evaluation of the disease, but also provides a new perspective for the clinical management of LN. Brief Description of the Drawings

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 For single-cell analysis of the CD8 / CD4 ratio;

[0017] Figure 2 For single-cell analysis of the naive B cell κ / λ ratio;

[0018] Figure 3 For the strategy of calculating the CD8 / CD4 ratio; wherein, A is the cell circled by feature analysis according to the cell size; B is the single cell separated by excluding doublets and clumps; C is the identified live cell; D is the CD3+CD19-T cell and CD3-CD19+B cell respectively identified from the live cells; E is the CD4+T cell and CD8+T cell circled from the CD3+CD19-T cells using CD4 and CD8;

[0019] Figure 4 For the strategy of calculating the naive B cell κ / λ ratio; wherein, A is the cell circled by feature analysis according to the cell size; B is the single cell separated by excluding doublets and clumps; C is the identified live cell; D is the CD3+CD19-T cell and CD3-CD19+B cell respectively identified from the live cells; E is the naive B cell circled from the CD3-CD19+B cells using CD27-IgM+; F is the cell circled by detecting the κ and λ light chains of the naive B cell subtypes;

[0020] Figure 5 For flow cytometry detection of the CD8 / CD4 ratio;

[0021] Figure 6 For flow cytometry detection of the naive B cell κ / λ ratio;

[0022] Figure 7 For the ROC curve. Detailed implementation manners

[0023] The various exemplary implementation manners of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, characteristics, and implementation schemes of the present invention.

[0024] It should be understood that the terms used in the present invention are only for describing specific embodiments and are not intended to limit the present invention. Additionally, for the numerical ranges in the present invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any intermediate value within any stated value or stated range, as well as each smaller range between any other stated value or intermediate value within the stated range, is also included in the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0025] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although the present invention only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the said documents. In case of conflict with any incorporated document, the content of this specification shall prevail.

[0026] Without departing from the scope or spirit of the present invention, various modifications and variations can be made to the specific embodiments of the present invention specification, which are obvious to those skilled in the art. Other embodiments obtained from the specification of the present invention are obvious to those skilled in the art. The specification and examples of the present invention are merely exemplary.

[0027] Regarding the use of "comprising", "including", "having", "containing", etc. herein, they are all open-ended terms, meaning including but not limited to.

[0028] Example 1

[0029] I. Construction of the prediction model

[0030] 1. Collect 10 ml of peripheral blood using an EDTA anticoagulant tube, centrifuge at 3000 rpm for 10 minutes, and retain and freeze the upper plasma at -80°C for later use. Extract peripheral blood mononuclear cells (PBMC) using density gradient centrifugation, freeze them at -80°C, and then transfer them to liquid nitrogen.

[0031] 2. Antibody combinations labeled with fluorescent dyes: The antibody combination for labeling the CD8 / CD4 ratio is CD3-BV605, CD19-APCCy7, CD4-BV510, CD8-PE; the antibody combination for labeling naive B cells is CD3-BV605, CD19-APCCy7, CD27-BV510, IgM-APC, κ-FITC, and λ-PE.

[0032] 3. Fluorescent dye-labeled antibody staining procedure: Cryopreserved PBMCs were rapidly thawed in a 37°C water bath and resuspended in complete RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. All subsequent washing steps were performed using PBS containing 2% FBS (P2F). Fc receptors were blocked with an FcR blocking reagent (130-059-901, Miltenyi Biotec, Germany) at 4°C for 10 minutes to prevent non-specific staining. Then, the cells were incubated with the antibody cocktail in the dark at 4°C for 30 minutes. Finally, dead cells were stained with 7-AAD viability staining solution (BioLegend, USA). All samples were analyzed using a Guava easyCyte cytometer. Detailed analysis was performed using FlowJo software version 10.8.1 (BD Life Sciences, USA).

[0033] 4. Flow cytometry gating strategy: First, cells were gated based on their size characteristics (see Figure 3 and Figure 4 ), and then doublets and clumps were excluded to isolate single cells. Next, live cells were identified. Then, CD3+CD19-T cells and CD3-CD19+B cells were gated using CD19 and CD3, respectively:

[0034] (1) Subsequently, CD4+T cells and CD8+T cells were gated from CD3+CD19-T cells using CD4 and CD8;

[0035] (2) Subsequently, CD27 and IgM were gated in CD3-CD19+B cells, and naive B cell subtypes were gated using CD27-IgM+. Finally, the κ and λ light chains of naive B cell subtypes were detected.

[0036] 5. Construction of a prediction model: A binary logistic regression model was constructed using the CD8 / CD4 ratio and the naive B cell κ / λ ratio with SPSS software.

[0037] 6. Model Effect Evaluation and Threshold Calculation: Receiver Operating Characteristic Curve (ROC curve) analysis is used to evaluate the model's effect and calculate the reference value range. Specifically, the present invention measures the diagnostic accuracy and discrimination ability of the model by plotting the ROC curve and calculating the Area Under the Curve (AUC) value. The ROC curve is a tool for evaluating the performance of a binary classification model. It shows the dynamic relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR) by comparing the probability threshold predicted by the model with the actual results. The AUC value, that is, the area under the ROC curve, ranges from 0 to 1, and the closer the value is to 1, the stronger the classification ability of the model. Finally, the diagnostic threshold is determined according to Youden's J statistic.

[0038] II. Analysis Results

[0039] (1) Discovery of Specific Immune Cell Changes in LN: Using 6 cases each of healthy controls (HC), SLE-NKI, and LN patients, peripheral blood mononuclear cells were obtained and subjected to single-cell transcriptome sequencing to analyze the abnormal changes in specific immune cells in LN patients. It was found that the CD8 / CD4 ratio ( Figure 1 ) and the κ / λ ratio of naive B cells ( Figure 2 ) in LN patients were elevated, higher than those in SLE-NKI and HC, and the differences were statistically significant, indicating that the elevation of the CD8 / CD4 ratio and the κ / λ ratio of naive B cells may be specific immune cell changes in LN patients.

[0040] (2) Verification of Specific Immune Cell Changes in LN: Flow cytometry was performed on blood samples from 14 SLE-NKI and 30 LN patients to verify the specific immune cell changes in LN patients. The results showed that the CD8 / CD4 ratio in LN patients was higher than that in SLE-NKI patients, and the difference was statistically significant ( Figure 5 ). The κ / λ ratio of naive B cells in LN patients was higher than that in SLE-NKI patients, and the difference was statistically significant ( Figure 6 ).

[0041] III. Construction of a Prediction Model and Model Evaluation

[0042] Using SPSS software, binary logistic regression analysis was performed on two indicators, the CD8 / CD4 ratio and the κ / λ ratio of naive B cells, to obtain a logistic regression model:

[0043] Logit(p) = -5.598 + 2.545*(CD8 / CD4 ratio) + 2.003*(naive B cell κ / λ ratio)

[0044] Model evaluation: Analyzing the AUC using the ROC curve, both of the two indicators in the constructed logistic regression model are statistically significant, and the AUC of the logistic regression model combining the two indicators is as high as 0.855 (95% CI: 0.745 - 0.965)( Figure 7 ).

[0045] Calculating the critical value: Using the Youden index (J = sensitivity + specificity - 1) to calculate the critical value, and the result of the critical value is 0.66938, indicating that if the model test result is higher than this critical value, it is considered that the patient has a high risk of LN progression; if it is lower than this critical value, it is considered that the current patient has a low risk of progressing to LN.

[0046] The embodiments described above are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A biomarker for screening and / or diagnosing the progression of systemic lupus erythematosus to lupus nephritis, characterized in that: The biomarkers include CD8 / CD4 ratio and naive B cell κ / λ ratio.

2. A product for predicting the progression of systemic lupus erythematosus to lupus nephritis, characterized in that: Comprising a reagent for detecting the protein expression amount contained in the biomarker of claim 1.

3. The product according to claim 2, characterized in that The products include test kits, test strips or instrument platforms.

4. Use of a reagent for detecting the protein expression level contained in the biomarker according to claim 1 in preparing a kit for screening and / or diagnosing the progression of systemic lupus erythematosus to lupus nephritis.

5. A prediction model for evaluating the progression of systemic lupus erythematosus to lupus nephritis, characterized in that: The prediction model is based on the expression level of the biomarker according to claim 1, and a binary logistic regression analysis is performed to obtain a LogitP value, and the progression of systemic lupus erythematosus to lupus nephritis is predicted based on the LogitP value; wherein the calculation formula of the LogitP value is: Logitp=-5.598+2.545*(CD8 / CD4 ratio)+2.003*(naive B cell κ / λ ratio).

6. The prediction model according to claim 5, characterized in that When the LogitP value is higher than the critical value of 0.66938, the risk of systemic lupus erythematosus progressing to lupus nephritis is higher; when the LogitP value is lower than the critical value of 0.66938, the risk of systemic lupus erythematosus progressing to lupus nephritis is lower.

7. A kit for non-invasive screening and / or diagnosis of progression of systemic lupus erythematosus to lupus nephritis, characterized in that: The kit performs binary logistic regression analysis based on the CD8 / CD4 ratio and the immature B cell κ / λ ratio in the peripheral blood sample of the test subject to obtain a LogitP value; when the LogitP value is higher than the critical value of 0.66938, the risk of the test subject progressing to lupus nephritis is higher; when the LogitP value is lower than the critical value of 0.66938, the risk of the test subject progressing to lupus nephritis is lower.

8. The kit according to claim 7, characterized in that The LogitP value calculation formula is: Logitp=-5.598+2.545*(CD8 / CD4 ratio)+2.003*(naive B cell κ / λ ratio).

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