A biomarker for evaluating progression of systemic lupus erythematosus to lupus nephritis, a prediction model and application thereof

Abstract

The application discloses a kind of biomarkers for evaluating systemic lupus erythematosus to lupus nephritis progression, prediction model and application, belong to lupus nephritis prediction technical field.The application provides a kind of for evaluating systemic lupus erythematosus to lupus nephritis progression prediction model, the prediction model is according to the ratio of biomarker CD8 / CD4 and the κ / λ ratio of naive B cell, carries out binary Logistic regression analysis, obtains LogitP value, by comparing the high and low of the LogitP value and critical value 0.66938, predicts systemic lupus erythematosus to lupus nephritis progression situation.The prediction model constructed in the application aims to identify the individuals in lupus patients who may develop into lupus nephritis.This innovative model not only provides important guidance in the diagnosis, prognosis prediction and efficacy evaluation of the disease, but also provides a new perspective for the clinical management of lupus nephritis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lupus nephritis prediction, in particular to a biomarker for evaluating the progression of systemic lupus erythematosus to lupus nephritis, a prediction model and application. BACKGROUND

[0002] Systemic lupus erythematosus (SLE) is a chronic autoimmune disease mainly affecting women of childbearing age, with diverse and varying clinical manifestations. Lupus nephritis (LN) is the most common target organ damage in SLE. It has been reported that about 60% of adult SLE patients will have symptoms of kidney involvement during their course, and some LN patients may be asymptomatic or have hidden 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, given the high risk of SLE progression to LN, establishing an effective method to evaluate the likelihood of LN progression in SLE patients is of great significance for early identification and diagnosis of LN and is crucial for guiding clinical treatment decisions. SUMMARY

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

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

[0005] Technical solution one: a biomarker for screening and / or diagnosing the progression of systemic lupus erythematosus to lupus nephritis, the biomarker comprising CD8 / CD4 ratio and naive B cell kappa / lambda ratio.

[0006] Technical solution two: a product for predicting the progression of systemic lupus erythematosus to lupus nephritis, comprising a reagent for detecting the expression level of the biomarker.

[0007] Further, the product comprises a kit, test paper or instrument platform.

[0008] Technical solution three: use of a reagent for detecting the expression level of the protein contained in 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, wherein the prediction model is obtained by binary Logistic regression analysis according to the expression amount of the biomarker, and the LogitP value is obtained by binary Logistic regression analysis according to the expression amount of the biomarker, and the LogitP value is used to predict the progression of systemic lupus erythematosus to lupus nephritis; wherein the calculation formula of the LogitP value is: Logitp = -5.598 + 2.545*(CD8 / CD4 ratio) + 2.003*(naive B cell kappa / lambda ratio).

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

[0011] Technical solution five: a kit for non-invasive screening and / or diagnosis of the progression of systemic lupus erythematosus to lupus nephritis, wherein the kit is used to obtain the LogitP value by binary Logistic regression analysis according to the CD8 / CD4 ratio and the naive B cell kappa / lambda ratio in the peripheral blood sample of the subject; when the LogitP value is higher than the critical value 0.66938, the risk of the subject progressing to lupus nephritis is higher; when the LogitP value is lower than the critical value 0.66938, the risk of the subject 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 kappa / lambda ratio).

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

[0014] The present application develops a non-invasive prediction model to predict the risk of SLE patients progressing to LN, which is of great significance for improving the prognosis of SLE patients and guiding individualized treatment. The model integrates potential biomarkers in the circulation of patients, aiming to provide a prediction tool to help clinicians more effectively manage SLE patients, especially those at high risk of developing LN. The present application comprehensively compares 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 application constructs a non-invasive clinical prediction model to identify individuals who may develop LN in lupus patients. This innovative model not only provides important guidance 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 DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

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

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

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

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

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

[0022] Figure 7 For ROC curve. DETAILED DESCRIPTION

[0023] The various exemplary embodiments of the present application will now be described in detail, which should not be considered as limiting the present application, but should be understood as a more detailed description of some aspects, characteristics and embodiments of the present application.

[0024] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. Additionally, for a range of values of, for example, a parameter, an individual value or subrange within that range is also specifically disclosed. Each of the smaller ranges is also individually and specifically disclosed. The upper and lower limits of these smaller ranges can independently be included or excluded in the range, and are also endpoints of the range, subject to any specifically excluded endpoint.

[0025] Unless defined otherwise, 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 application belongs. Although preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application. All documents mentioned herein are incorporated by reference to disclose and describe in detail the methods and / or materials which are related to the present application. In the case of conflict between the present specification and any document incorporated herein by reference, the present specification will control.

[0026] Many modifications and variations of the present application described in the specific embodiments of the application can be made by those skilled in the art without departing from the spirit or scope of the application. Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application. The specification and examples are illustrative only.

[0027] As used herein, the terms "comprises", "comprising", "includes", "including", "has", "having", "contains", "containing", or variations thereof, are intended to be open-ended terms that mean including, but not limited to.

[0028] Example 1

[0029] I. Construction of predictive model

[0030] 1. 10 ml peripheral blood was collected in EDTA anticoagulant tubes, centrifuged at 3000 rpm for 10 minutes, and the upper plasma was reserved for storage at -80°C. Peripheral blood mononuclear cells (PBMCs) were extracted using density gradient centrifugation, and stored at -80°C before being transferred to liquid nitrogen.

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

[0032] 3. Fluorochrome-labeled antibody staining procedure: Cryopreserved PBMCs were quickly 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 at 4 °C for 10 min using FcR blocking reagent (130-059-901, Miltenyi Biotec, Germany) to prevent non-specific staining. Then, the cells were incubated with antibody combinations at 4 °C for 30 min in the dark. Finally, dead cells were stained using 7-AAD viability stain 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, the cells were characterized according to their size to gate the cells (see Fig. 2A). Figure 3 and Figure 4 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 (naive B cell) subsets were gated using CD27-IgM+.

[0036] 5. Construction of prediction model: Binary logistic regression model was constructed using CD8 / CD4 ratio and naive B cell kappa / lambda ratio by SPSS software.

[0037] 6. Model effect evaluation and critical value calculation: Receiver Operating Characteristic Curve (ROC curve) analysis is used to evaluate the effect of the model, and the reference value range is calculated. Specifically, the present application measures the diagnostic accuracy and discrimination ability of the model by drawing the ROC curve and calculating the Area Under the Curve (AUC) value. 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 result. The AUC value, i.e. 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 critical value is determined according to the Youden's J statistic.

[0038] II. Analysis results

[0039] (1) Find the changes of LN-specific immune cells: Using 6 healthy controls (HC), 6 SLE-NKI and 6 LN patients, after obtaining peripheral blood mononuclear cells, single-cell transcriptome sequencing was performed to analyze the abnormal changes of LN-specific immune cells in LN patients. It was found that the CD8 / CD4 ratio ( Figure 1 ) and the immature B cell κ / λ ratio ( Figure 2 ) of LN patients were higher than those of SLE-NKI and HC, and the difference was statistically significant, indicating that the increase of CD8 / CD4 ratio and immature B cell κ / λ ratio may be a specific immune cell change in LN patients.

[0040] (2) Verification of LN-specific immune cell changes: 14 SLE-NKI and 30 LN patient blood samples were used for flow cytometry to verify the specific immune cell changes in LN patients. The results showed that the CD8 / CD4 ratio of LN patients was higher than that of SLE-NKI patients, and the difference was statistically significant ( Figure 5 ). The κ / λ ratio of immature 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 prediction model and model evaluation

[0042] SPSS software was used for binary logistic regression analysis of CD8 / CD4 ratio and immature B cell κ / λ ratio, and the logistic regression model was obtained:

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

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

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

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

Claims

1. A prediction model for assessing progression of systemic lupus erythematosus to lupus nephritis, characterized by, The prediction model is obtained by binary Logistic regression analysis according to the expression amount of the biomarker, and a LogitP value is obtained, and the progression of systemic lupus erythematosus to lupus nephritis is predicted according to 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 kappa / lambda ratio); The biomarker includes CD8 / CD4 ratio and immature B cell κ / λ ratio.

2. The predictive model of claim 1, wherein, The product also includes a product for predicting the progression of systemic lupus erythematosus to lupus nephritis; the product includes a reagent for detecting the expression amount of the protein contained in the biomarker.

3. The prediction model of claim 2, wherein, The product includes a kit, a test paper or an instrument platform.

4. The predictive model of claim 1, wherein, When the LogitP value is higher than the critical value 0.66938, the risk of the progression of systemic lupus erythematosus to lupus nephritis is higher; when the LogitP value is lower than the critical value 0.66938, the risk of the progression of systemic lupus erythematosus to lupus nephritis is lower.

5. The use of a reagent for detecting the expression amount of the protein contained in the biomarker in claim 1 in the preparation of a kit for screening and / or diagnosing the progression of systemic lupus erythematosus to lupus nephritis.

6. A device for non-invasive screening and / or diagnosis of progression of systemic lupus erythematosus to lupus nephritis, characterized in that, The device carries out binary Logistic regression analysis according to the CD8 / CD4 ratio and the immature B cell κ / λ ratio in the peripheral blood sample of the subject to be tested to obtain a LogitP value; when the LogitP value is higher than the critical value 0.66938, the risk of the progression of the subject to be tested to lupus nephritis is higher; when the LogitP value is lower than the critical value 0.66938, the risk of the progression of the subject to be tested to lupus nephritis is lower. The LogitP value is calculated by the formula: Logitp = -5.598 + 2.545 (CD8 / CD4 ratio) + 2.003 (naive B cell kappa / lambda ratio).

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