System and kit for diagnosing behcet disease through expression quantity of molecular marker

Abstract

The invention provides a system for diagnosing Behcet's disease through expression quantity of a molecular marker, application of a reagent for quantitatively detecting the expression quantity of the molecular marker in preparation of a kit for diagnosing Behcet's disease, the kit for diagnosing Behcet's disease, application of the molecular marker in preparation of the kit for diagnosing Behcet's disease and application of the molecular marker in preparation of the kit for diagnosing Behcet's disease. The molecular marker comprises a combination of an AMT autoantibody, a PRSS8 autoantibody, a GJC3 autoantibody, a TAF15 autoantibody and an HIPK2 autoantibody. According to the technical scheme, the molecular marker with high specificity and high sensitivity and a curative effect prediction model are provided for diagnosis and treatment of Behcet's disease.

Description

Technical Field

[0001] This disclosure relates to the field of biomedical technology, specifically to a system for diagnosing Behcet's disease by measuring the expression level of molecular markers, the use of reagents for quantitatively detecting the expression level of molecular markers in the preparation of kits for diagnosing Behcet's disease, kits for diagnosing Behcet's disease, and the use of molecular markers in the preparation of kits for diagnosing Behcet's disease. Background Technology

[0002] Behcet's disease is a systemic, chronic, vasculitic disease. Clinically, it is characterized by prominent oral ulcers, genital ulcers, conjunctivitis, and skin lesions. The disease commonly affects the nervous system, digestive tract, lungs, kidneys, and epididymis, and once organ involvement occurs, it often recurs, leading to serious complications such as thrombosis, blindness, Behcet's encephalopathy, and gastrointestinal perforation, even resulting in death.

[0003] Currently, the diagnosis of Behçet's disease is primarily based on clinical presentation after ruling out other potential causes, without specific laboratory, histopathological, or genetic findings. Furthermore, there is significant geographical variation in disease prevalence and presentation. Therefore, diagnosing Behçet's disease in patients presenting only with major organ involvement, such as posterior uveitis, neurological, vascular, and gastrointestinal manifestations, can be challenging. While the appearance of other disease manifestations can aid in a definitive diagnosis, this can take months or even years. In some patients, the presentation of other diseases may be limited, further complicating diagnosis.

[0004] The lack of ideal, specific diagnostic antibodies in serum makes the diagnosis and early treatment of atypical Behçet's disease a challenge for rheumatologists. Therefore, given the current situation, there is an urgent need to find early-stage, specific molecular markers for Behçet's disease. Summary of the Invention

[0005] The purpose of this disclosure is to provide highly specific and sensitive molecular markers and systems for Behçet's disease.

[0006] On one hand, this disclosure provides a system for diagnosing Behcet's disease by the expression level of molecular markers. The system includes a computing device, an input device for inputting the expression levels of molecular markers of an individual patient with Behcet's disease, and an output device for outputting the diagnostic results of Behcet's disease. The molecular markers include a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody, and HIPK2 autoantibody.

[0007] The computing device includes a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program stored in the memory to implement a modeling algorithm and an algorithm for a discriminant function as shown in equation (1); the modeling algorithm is a support vector machine algorithm and / or a least partial squares algorithm.

[0008] F(c)=sgn[f1(c1)+f2(c2)+f3(c3)+f4(c4)+f5(c5)+b] Formula (1)

[0009] In equation (1), F(c) represents the diagnosis result of Behcet's disease. A return value of 1 for F(c) indicates support, and a return value of -1 indicates rejection. c1, c2, c3, c4 and c5 represent the absolute expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody, respectively. f1(c1), f2(c2), f3(c3), f4(c4) and f5(c5) are kernel functions trained according to the modeling algorithm, and b is the critical score value trained according to the modeling algorithm.

[0010] Optionally, the AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody, and HIPK2 autoantibody are derived from serum samples of the patients with Behcet's disease.

[0011] Optionally, the system further includes a detection device for detecting the expression level of a molecular marker, the detection device including a detection chip for the expression level of the molecular marker and a chip signal reader.

[0012] Optionally, the detection chip for the expression levels of the molecular markers includes antigen reagents for detecting the expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody, and HIPK2 autoantibody, respectively.

[0013] Optionally, the antigen reagent includes AMT protein antigen, PRSS8 protein antigen, GJC3 protein antigen, TAF15 protein antigen, and HIPK2 protein antigen.

[0014] Optionally, in formula (1), the units of c1, c2, c3, c4, and c5 are ng / mL, f1(c1) = 0.0783 × c1, f2(c2) = 0.0925 × c2, f3(c3) = 0.0573 × c3, f4(c4) = 0.1047 × c4, f5(c5) = 0.1532 × c5, and b is -21.5296.

[0015] On the other hand, this disclosure provides the use of a reagent for quantitatively detecting the expression level of a molecular marker in the preparation of a kit for diagnosing Behçet's disease, wherein the molecular marker is a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody.

[0016] On the other hand, this disclosure also provides a kit for diagnosing Behçet's disease, which includes reagents for quantitatively detecting the expression levels of molecular markers, said molecular markers being a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody.

[0017] Optionally, the process of quantitatively detecting the expression level of molecular markers includes:

[0018] S1. Obtain serum samples from patients with Behçet's disease;

[0019] S2. Determine the expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody in the serum sample.

[0020] On the other hand, this disclosure provides the use of molecular markers in the preparation of kits for the diagnosis of Behçet's disease, said molecular markers being a combination of AMT autoantibodies, PRSS8 autoantibodies, GJC3 autoantibodies, TAF15 autoantibodies and HIPK2 autoantibodies.

[0021] Through the above technical solutions, this disclosure has discovered that the combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody can serve as a molecular marker for the diagnosis of Behçet's disease. This provides a highly specific and sensitive molecular marker and diagnostic model for the individualized diagnosis and treatment of Behçet's disease, and provides a novel, efficient and non-invasive auxiliary tool for the clinical development of diagnostic protocols for Behçet's disease.

[0022] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0024] Figure 1 This is a graph showing the effectiveness of different antigen proteins in differentiating healthy controls from patients with Behcet's disease.

[0025] Figure 2It is a linear curve after serial dilution of the standard.

[0026] Figure 3 This is a Conner plot of a microarray scan TIFF image from a Behcet's disease patient.

[0027] Figure 4 This is a consistency graph of the repeated signals from the chip technology of healthy control samples.

[0028] Figure 5 This is a distribution diagram of the chip signals from a Behçet's disease patient sample and a healthy control sample.

[0029] Figure 6 These are diagnostic AUC test charts for different combinations of biomarkers. Detailed Implementation

[0030] The following provides a detailed description of specific embodiments of this disclosure. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit this disclosure.

[0031] On the one hand, this disclosure provides a system for diagnosing Behçet's disease by the expression level of molecular markers. The system includes a computing device, an input device for inputting the expression level of molecular markers of an individual patient with Behçet's disease, and an output device for outputting the diagnostic results of Behçet's disease.

[0032] The molecular markers include a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody;

[0033] The computing device includes a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program stored in the memory to implement a modeling algorithm and an algorithm for a discriminant function as shown in equation (1); the modeling algorithm is a support vector machine algorithm and / or a least partial squares algorithm.

[0034] F(c)=sgn[f1(c1)+f2(c2)+f3(c3)+f4(c4)+f5(c5)+b] Formula (1)

[0035] In equation (1), F(c) represents the diagnosis result of Behcet's disease. A return value of 1 for F(c) indicates support, and a return value of -1 indicates rejection. c1, c2, c3, c4 and c5 represent the absolute expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody, respectively. f1(c1), f2(c2), f3(c3), f4(c4) and f5(c5) are kernel functions trained according to the modeling algorithm, and b is the critical score value trained according to the modeling algorithm.

[0036] Optionally, the AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody, and HIPK2 autoantibody are derived from serum samples of the patients with Behcet's disease.

[0037] Optionally, the system further includes a detection device for detecting the expression level of a molecular marker, the detection device including a detection chip for the expression level of the molecular marker and a chip signal reader.

[0038] Optionally, the detection chip for the expression levels of the molecular markers includes antigen reagents for detecting the expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody, and HIPK2 autoantibody, respectively.

[0039] Optionally, the antigen reagent includes AMT protein antigen, PRSS8 protein antigen, GJC3 protein antigen, TAF15 protein antigen, and HIPK2 protein antigen.

[0040] Optionally, in formula (1), the units of c1, c2, c3, c4, and c5 are ng / mL, f1(c1) = 0.0783 × c1, f2(c2) = 0.0925 × c2, f3(c3) = 0.0573 × c3, f4(c4) = 0.1047 × c4, f5(c5) = 0.1532 × c5, and b is -21.5296.

[0041] On the other hand, this disclosure provides the use of a reagent for quantitatively detecting the expression level of a molecular marker in the preparation of a kit for diagnosing Behçet's disease, wherein the molecular marker is a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody.

[0042] On the other hand, this disclosure provides a kit for diagnosing Behçet's disease, the kit including reagents for quantitatively detecting the expression levels of molecular markers, said molecular markers being a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody.

[0043] Optionally, the process of quantitatively detecting the expression level of molecular markers includes:

[0044] S1. Obtain serum samples from patients with Behçet's disease;

[0045] S2. Determine the expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody in the serum sample.

[0046] On the other hand, this disclosure provides the use of molecular markers in the preparation of kits for the diagnosis of Behçet's disease, said molecular markers being a combination of AMT autoantibodies, PRSS8 autoantibodies, GJC3 autoantibodies, TAF15 autoantibodies and HIPK2 autoantibodies.

[0047] The present disclosure is further described in detail below through examples. All raw materials used in the examples are commercially available.

[0048] Example 1

[0049] This example illustrates the discovery of molecular markers and the construction of models.

[0050] 1. Subject sample screening: Inclusion criteria for BD patients: 50 BD patients were selected according to the 1990ISG / 2014ICBD Behçet's disease diagnostic criteria. EDTA anticoagulated plasma with complete clinical data and medical records was obtained and aliquoted on the same day, numbered, and frozen at -80℃ for later use.

[0051] The research protocol of this embodiment has been approved by the Ethics Committee of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences.

[0052] The molecular markers selected in this disclosure are all newly discovered autoantibodies in the serum of patients with Behçet's disease, as shown in Table 1:

[0053] Table 1

[0054] UNIPROT ID protein name P48728 AMT(Aminomethyltransferase,mitochondrial) Q16651 PRSS8 (Prostasin) Q8NFK1 GJC3(Gap junction gamma-3protein) Q92804 TAF15(TATA-binding protein-associated factor 2N) Q9H2X6 HIPK2 (Homeodomain-interacting protein kinase 2)

[0055] 2. Testing process:

[0056] (1) Sample dilution: Add 2.1 mL of 1% Casein to 39.9 mL of sample dilution solution and mix well to prepare 0.05% Casein solution. Add 1 μL of the biological sample to be tested to 199 μL of 0.05% Casein, seal with a sealing machine, mix by shaking with a mixer, and centrifuge at 4000 rpm for 1 minute to form a 200-fold diluted biological sample;

[0057] (2) Sample loading: Add 90 μL of the diluted biological sample to the corresponding reaction well according to the layout requirements;

[0058] (3) First incubation: Seal the reaction wells and place them on the incubation module of the automated instrument. The incubation conditions are controlled by software and the incubation lasts for 1 hour.

[0059] (4) First plate washing: Tear off the membrane from the reaction wells and place them in an automatic plate washer for washing. The washing solution includes the following components: 130-137mM sodium chloride, 2.5-2.7mM potassium chloride, 3.8-4.3mM disodium hydrogen phosphate, 1.2-1.4mM potassium dihydrogen phosphate, 0.05-1% Tween-20 v / v, 0.05-0.1% Proclin 950 v / v, pH 7.2-7.6;

[0060] (5) Add secondary antibody: Under light-protected conditions (lights off), use a 50mL centrifuge tube to add 12μL of mouse anti-human IgG (MouseAnti-Human IgG Fc, Southern Biotech) to 18mL of secondary antibody dilution buffer, dilute 1500 times, mix well and set aside. The secondary antibody buffer blocking solution includes the following components: 130-137mM sodium chloride, 2.5-2.7mM potassium chloride, 3.8-4.3mM disodium hydrogen phosphate, 1.2-1.4mM potassium dihydrogen phosphate, 0.05-1% Tween-20 v / v, 0.05-0.1% Proclin 950 v / v, 0.5-1% D-mannitol w / v and 0.1-1% sodium caseinate w / v;

[0061] (6) Second incubation: Seal the reaction wells and place them on the incubation module of the automated instrument. The incubation conditions are controlled by software and the incubation lasts for 1 hour.

[0062] (7) Second washing: Tear off the film from the reaction wells and place them in an automatic plate washer for washing;

[0063] (8) Imaging scan: The intensity of fluorescence signals at the sample wells is acquired using image acquisition technology, such as... Figure 3 (tiff) Figure 4 (Technological consistency) Figure 5 As shown (signal distribution of positive and negative sample chips);

[0064] (9) Data Analysis: Construct serial dilution curves for the standards, with the signal value on the ordinate and the concentration (ng / mL) on the abscissa, such as... Figure 2 As shown. The actual concentration (ng / mL) of the molecular marker in the sample is calculated based on the measured signal value. The formula for converting the signal value to concentration is:

[0065] Protein autoantibody concentration = (signal detection value + 0.03650) / 0.03389;

[0066] The actual concentrations of the measured molecular markers are used as the dataset.

[0067] 3. Construction of a diagnostic model for Behçet's disease:

[0068] The above dataset was randomly divided into two datasets. One dataset was used as the training set for diagnosing Behcet's disease, and the other dataset was used as the validation set for diagnosing Behcet's disease. The training set was used to train the calculated coefficients and thresholds of the components (absolute concentrations of molecular markers) in the model, and the validation set was used for evaluation. The discriminant function of the model is shown in Equation (1):

[0069] F(c)=sgn[f1(c1)+f2(c2)+f3(c3)+f4(c4)+f5(c5)+b] Formula (1)

[0070] In equation (1), F(c) represents the diagnosis result of Behcet's disease, with a return value of 1 indicating support and a return value of -1 indicating rejection; c1, c2, c3, c4, and c5 represent the absolute expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody, and HIPK2 autoantibody, respectively; f1(c1), f2(c2), f3(c3), f4(c4), and f5(c5) are based on the modeling algorithm described above. The kernel function obtained through training, b is the critical score value obtained based on the modeling algorithm; the units of c1, c2, c3, c4 and c5 are ng / mL, f1(c1) = 0.0783 × c1, f2(c2) = 0.0925 × c2, f3(c3) = 0.0573 × c3, f4(c4) = 0.1047 × c4, f5(c5) = 0.1532 × c5, and b is -21.5296; that is to say, the discriminant function is specifically:

[0071] F(c)=sgn[0.0783×c1+0.0925×c2+0.0573×c3+0.1047×c4+0.1532×c5-21.5296] Equation (1).

[0072] Example 2

[0073] This embodiment further validates the Behçet's disease diagnostic model constructed in Example 1:

[0074] The following are different combinations of antigen proteins:

[0075] (1)AMT, TAF15; (2) AMT, TAF15, PRSS8; (3) AMT, TAF15, GJC3; (4) AMT, TAF15, HIPK2; (5) AMT, TAF15, PRSS 8. GJC3; (6) AMT, TAF15, PRSS8, HIPK2; (7) AMT, TAF15, GJC3, HIPK2; (8) AMT, TAF15, PRSS8, GJC3, HIPK2.

[0076] The performance of the above combinations in patients with Behçet's disease and healthy controls is shown in Table 3. AUC is as follows: Figure 6 As shown.

[0077] Table 3

[0078] Logo combination AUC Cutoff value Specificity Sensitivity 1 0.757 1.670 0.854 0.600 2 0.728 1.939 0.771 0.660 3 0.747 1.842 0.896 0.560 4 0.803 1.969 0.938 0.580 5 0.740 2.103 0.917 0.520 6 0.811 2.127 0.854 0.720 7 0.805 2.019 0.875 0.700 8 0.815 2.180 0.854 0.760

[0079] As can be seen from the above scheme, the combination of AMT autoantibody, TAF15 autoantibody, PRSS8 autoantibody, GJC3 autoantibody, and HIPK2 autoantibody provided in this disclosure can serve as a molecular marker for the diagnosis of Behçet's disease, and has high sensitivity and high specificity, which is of great significance for the clinical development of diagnostic protocols for Behçet's disease.

[0080] The preferred embodiments of this disclosure have been described in detail above. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0081] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0082] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A system for diagnosing Behcet's disease by the amount of expression of a molecular marker, characterized by, The system comprises a computing device, an input device for inputting the expression amount of molecular markers of a Bechet disease patient, and an output device for outputting the diagnosis result of the Bechet disease; wherein the molecular markers comprise a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody. The computing device comprises a memory and a processor, the memory stores a computer program, and the processor is configured to execute the computer program stored in the memory to implement a modeling algorithm and an algorithm of a discriminant function as shown in formula (1); the modeling algorithm is a support vector machine algorithm and / or a least square algorithm; F(c) = sgn[f1(c1) + f2(c2) + f3(c3) + f4(c4) + f5(c5) + b] formula (1) In formula (1), F(c) represents the diagnosis result of the Bechet disease, and the return value of F(c) is 1, indicating support, and the return value is -1, indicating rejection; c1, c2, c3, c4 and c5 respectively represent the absolute expression amount of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody; f1(c1), f2(c2), f3(c3), f4(c4) and f5(c5) are kernel functions trained according to the modeling algorithm, and b is a critical score value trained according to the modeling algorithm.

2. The system of claim 1, wherein, The AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody are derived from serum samples of the Bechet disease patient.

3. The system of claim 1, wherein, The system further comprises a detection device for detecting the expression amount of molecular markers; the detection device comprises a detection chip for the expression amount of molecular markers and a chip signal reader.

4. The system of claim 3, wherein, The detection chip for the expression amount of molecular markers comprises antigen reagents for detecting the expression amount of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody, respectively.

5. The system of claim 4, wherein, The antigen reagents comprise AMT protein antigen, PRSS8 protein antigen, GJC3 protein antigen, TAF15 protein antigen and HIPK2 protein antigen.

6. The system of claim 1, wherein, In formula (1), the units of c1, c2, c3, c4 and c5 are ng / mL, f1(c1) = 0.0783 × c1, f2(c2) = 0.0925 × c2, f3(c3) = 0.0573 × c3, f4(c4) = 0.1047 × c4, f5(c5) = 0.1532 × c5, and b is -21.5296.

7. Use of a reagent for quantitatively detecting the expression amount of a molecular marker in the preparation of a kit for diagnosing Behcet's disease, characterized in that, The molecular markers are a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody.

8. A kit for the diagnosis of Behcet's disease, characterized in that, The kit comprises reagents for quantitatively detecting the expression amount of molecular markers, and the molecular markers are a combination of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody.

9. The kit of claim 8, wherein, The process of quantitatively detecting the expression amount of molecular markers comprises: S1. Obtain serum samples from patients with Behçet's disease; S2. Determine the expression levels of AMT autoantibody, PRSS8 autoantibody, GJC3 autoantibody, TAF15 autoantibody and HIPK2 autoantibody in the serum sample.

10. Use of a molecular marker for the manufacture of a kit for the diagnosis of Behcet's disease, characterized in that, The molecular markers are a combination of AMT autoantibodies, PRSS8 autoantibodies, GJC3 autoantibodies, TAF15 autoantibodies, and HIPK2 autoantibodies.

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