Application of plasma N4BP1, CREB1, UBAP2, DEAF1 autoantibody markers in the prognosis prediction of diffuse large B-cell lymphoma

Abstract

The application discloses application of self-antibodies and detection reagents thereof in preparation of a kit for evaluating drug treatment prognosis of diffuse large B-cell lymphoma, wherein the antibodies include at least one of anti-N4BP1, anti-CREB1, anti-UBAP2 or anti-DEAF1. The application can improve the curative effect prediction performance of an R-CHOP treatment scheme, so that a treatment scheme of a patient can be selected in a targeted manner.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to the application of autoantibodies and their detection reagents in the preparation of kits for assessing the prognosis of drug treatment for diffuse large B-cell lymphoma. Background Technology

[0002] Lymphomas can be classified pathologically into Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), and lymphocytic leukemia. Diffuse large B-cell lymphoma (DLBCL) is the most common type of NHL. DLBCL exhibits high heterogeneity, leading to significant differences in its clinical characteristics and prognosis. Commonly used prognostic risk stratification methods include the International Prognostic Index (IPI) proposed in 1993, as well as subsequent methods such as R-IPI and NCCN-IPI. However, these only include clinical indicators and cannot provide sufficient evidence for precision treatment. Furthermore, the applicability of these prognostic scores to the era of rituximab therapy remains controversial. The gene expression profile (GEP) subtyping method, proposed in 2000, is considered the gold standard for molecular subtyping of DLBCL, but its application is limited due to the complexity and high cost of experimental techniques.

[0003] Rituximab, an anti-CD20 monoclonal antibody, was approved by the FDA in 1997. R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) is now the standard first-line treatment for DLBCL, curing more than 50% of patients. However, 30-40% of patients still experience refractory or relapsed disease, especially those refractory to first-line immunotherapy. The overall objective response rate (ORR) is only 26%, and the complete response rate (CR) is 7%. Therefore, finding new prognostic biomarkers for DLBCL in the rituximab era has significant clinical guiding implications. Summary of the Invention

[0004] To address the aforementioned technical problems, improve the prognostic efficacy of diffuse large B-cell lymphoma, and increase the cure rate for patients with diffuse large B-cell lymphoma, this invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a biomarker for assessing the prognosis of drug treatment for diffuse large B-cell lymphoma, said biomarker being an autoantibody selected from at least one of anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1.

[0006] In a second aspect, the present invention provides the use of an autoantibody detection reagent in the preparation of a kit for assessing the prognosis of drug treatment for diffuse large B-cell lymphoma, wherein the autoantibody is selected from at least one of anti-N4BP1, anti-CREB1, anti-UBAP2 and anti-DEAF1.

[0007] In a third aspect, the present invention provides a kit for assessing the prognosis of drug treatment for diffuse large B-cell lymphoma, the kit comprising a detection reagent for an autoantibody selected from at least one of anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1.

[0008] In a fourth aspect, the present invention provides a system for evaluating the prognosis of drug treatment for diffuse large B-cell lymphoma, the system comprising:

[0009] The acquisition module is used to acquire samples from the subject; and

[0010] An evaluation module, connected to an acquisition module, is used to detect autoantibodies in a sample using the kit of the present invention, wherein the autoantibodies are selected from at least one of anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1.

[0011] In a fifth aspect, the present invention provides a method for assessing the prognosis of drug treatment for diffuse large B-cell lymphoma, the method comprising detecting an autoantibody selected from at least one of anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1.

[0012] In some implementations, the autoantibody is selected from any combination of two or more of anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1.

[0013] In some embodiments, the autoantibody is selected from any of the following combinations: anti-N4BP1 and anti-CREB1, anti-N4BP1 and anti-UBAP2, anti-N4BP1 and anti-DEAF1, anti-CREB1 and anti-UBAP2, anti-CREB1 and anti-DEAF1, anti-UBAP2 and anti-DEAF1, anti-N4BP1 and anti-CREB1 and anti-UBAP2, anti-N4BP1 and anti-CREB1 and anti-DEAF1, anti-N4BP1 and anti-UBAP2 and anti-DEAF1, anti-CREB1 and anti-UBAP2 and anti-DEAF1, anti-N4BP1 and anti-CREB1 and anti-UBAP2 and anti-DEAF1.

[0014] In some implementations, the autoantibodies are autoantibodies in peripheral blood. In other implementations, the autoantibodies are autoantibodies in serum or plasma.

[0015] In some implementations, the detection reagents for autoantibodies include reagents capable of quantitatively detecting autoantibodies (e.g., anti-N4BP1, anti-CREB1, anti-UBAP2, anti-DEAF1).

[0016] In some implementations, the detection reagent for autoantibodies includes a substance (e.g., a protein or fragment thereof) capable of specifically binding to autoantibodies for qualitative or quantitative detection.

[0017] In some implementations, the detection reagents for autoantibodies may be included in tools such as kits, chips, or test strips, for example, reagents capable of qualitatively or quantitatively detecting autoantibodies (e.g., proteins or peptides that specifically bind to autoantibodies and specific labeled secondary antibodies).

[0018] In some implementations, the tool may be a tool for high-throughput protein platforms (protein chips) and / or ELISA methods to detect autoantibodies using reagents for qualitative or quantitative detection of autoantibodies (e.g., specific proteins or peptides and specific labeled secondary antibodies) to assess the prognosis of drug treatment for diffuse large B-cell lymphoma.

[0019] In some implementation schemes, subjects with high levels of autoantibody expression have a better prognosis for drug treatment.

[0020] In some implementation schemes, the criteria for determining high autoantibody expression levels are as follows:

[0021] Detection of autoantibodies' OD by ELISA 450 Value, of which

[0022] Anti-N4BP1 OD450 > 1.31, preferably anti-N4BP1 OD450 > 1.89, more preferably anti-N4BP1 OD450 > 2.38, and / or

[0023] Anti-CREB1 OD450 > 0.89, preferably anti-CREB1 OD450 > 1.27, more preferably anti-CREB1 OD450 > 1.66, and / or

[0024] Anti-UBAP2 OD450 > 1.37, preferably anti-UBAP2 OD450 > 2.05, more preferably anti-UBAP2 OD450 > 2.49, and / or

[0025] Anti-DEAF1 OD450 > 0.789, preferably anti-DEAF1 OD450 > 1.44, and more preferably anti-DEAF1 OD450 > 1.89.

[0026] In some implementations, drug treatment for diffuse large B-cell lymphoma includes anti-CD20 antibody therapy. In other implementations, drug treatment for diffuse large B-cell lymphoma includes rituximab in combination with cyclophosphamide, doxorubicin, vincristine, and prednisone.

[0027] In some implementations, the prognosis of drug treatment for diffuse large B-cell lymphoma includes the 2-year relapse rate, progression-free survival, and overall survival after anti-CD20 antibody therapy. When autoantibody expression is elevated / high, the 2-year relapse rate, progression-free survival, and overall survival are lower after anti-CD20 antibody therapy for diffuse large B-cell lymphoma. In some implementations, high autoantibody expression (e.g., detected by ELISA of the autoantibody OD) is considered. 450 Subjects who are suitable for drug treatment (such as anti-CD20 antibody drug treatment) at the time of their diagnosis are eligible for such treatment.

[0028] In some implementations, the system further includes an output module for outputting results based on the detection data from the evaluation module.

[0029] In some implementations, the assessment module includes evaluating the prognosis of drug treatment by detecting the level of autoantibody expression.

[0030] In some implementation schemes, subjects with high levels of autoantibody expression have a good prognosis for drug treatment.

[0031] In some implementations, the autoantibody expression level includes the pre-treatment autoantibody expression level.

[0032] The beneficial effects of this invention are:

[0033] This invention selects autoantibodies that are easily detected and can better reflect the body's immune function, such as anti-N4BP1, anti-CREB1, anti-UBAP2, or anti-DEAF1. These single antibodies or antibody combinations are used as prognostic markers for diffuse large B-cell lymphoma, enabling dynamic monitoring of treatment efficacy, improving the predictive efficacy of the R-CHOP treatment regimen, and adding a prognostic risk stratification tool.

[0034] This invention, through research on anti-N4BP1, anti-CREB1, anti-UBAP2, or anti-DEAF1, helps to discover new drug resistance mechanisms and therapeutic targets for diffuse large B-cell lymphoma, providing new methods and technologies for the treatment of diffuse large B-cell lymphoma. Attached Figure Description

[0035] Figure 1 The results of COX regression analysis of autoantibody progression-free survival (PFS) during the small chip screening process of this invention are shown.

[0036] Figure 2 The results of OS-COX regression during the small chip screening process of this invention are shown.

[0037] Figure 3 The results of the lasso-cox regression test for autoantibody PFS during the small chip screening process of this invention are shown.

[0038] Figure 4 The results of the lasso-cox regression test of autoantibody OS during the small chip screening process of this invention are shown.

[0039] Figure 5 This paper illustrates the differential analysis of autoantibodies in the EFS24 relapse and non-relapse groups during the small chip screening process of the present invention;

[0040] Figure 6 The differential analysis of the autoantibody anti-CREB1 in the EFS24 relapse group and non-relapse group and the Kaplan-Meier survival curve are shown in the ELISA validation process of the present invention.

[0041] Figure 7 The differential analysis of the autoantibody anti-N4BP1 in the EFS24 relapse group and non-relapse group and the Kaplan-Meier survival curve are shown in the ELISA validation process of the present invention.

[0042] Figure 8 The differential analysis of the autoantibody anti-DEAF1 in the EFS24 relapse group and non-relapse group and the Kaplan-Meier survival curve are shown in the ELISA validation process of this invention.

[0043] Figure 9The differential analysis of the autoantibody anti-UBAP2 in the EFS24 relapse group and non-relapse group and the Kaplan-Meier survival curve are shown in the ELISA validation process of this invention.

[0044] Figure 10 The ROC curves of autoantibodies anti-N4BP1, anti-CREB1, anti-UBAP2, or anti-DEAF1 used in the ELISA verification process of this invention are shown to distinguish whether DLBCL has relapsed within two years. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions in the art or as recommended by the manufacturer. Unless otherwise specified, all methods are conventional. Unless otherwise defined, technical and scientific terms used herein have the same meaning as those familiar with the art.

[0046] abbreviation:

[0047] N4BP1: Nedd4-E3 ubiquitin protein ligase binds to chaperone protein 1;

[0048] CREB1: Cyclic adenosine monophosphate response element-binding protein 1;

[0049] DEAF1: Deformation-related regulatory factor;

[0050] UBAP2: Ubiquitin protein 2.

[0051] The reagents used in the embodiments of this invention are sourced from:

[0052] The high-throughput protein chip (HuProt™) was purchased from CDI LABS, catalog number CDIHP-004; the small chip was purchased from CDI LABS, catalog number CDIHP-005.PC; the ELISA kit was a self-made kit (not a commercially available kit) from the Beijing Key Laboratory for Clinical Research of Antitumor Molecular Targeted Drugs.

[0053] Example 1: Screening of autoantibodies

[0054] Twenty DLBCL patients who received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) were randomly selected and divided into two groups based on whether they relapsed within two years (EFS24): a good prognosis group (n=9) and a poor prognosis group (n=11). Blood autoantibodies were detected using a Huprot microarray, and the "limma" package in R was used to screen for differentially expressed proteins. The specific experimental procedure is as follows:

[0055] 1.1 Preparation of patient serum samples

[0056] All serum samples were collected between 2010 and 2020 before first-line treatment with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and after routine clinical examinations. After the addition of EDTA anticoagulant, the samples were centrifuged at 3000 rpm and 4°C for 10 min, the supernatant was collected, and stored at -80°C in 2 mL conical tubes. The samples were thawed once before chip detection.

[0057] 1.2 Differential Protein Screening Steps

[0058] 1.2.1 Chip preparation: Take the HuProt™ chip out of the -80℃ freezer, put it in the chip box, wrap it with plastic wrap, and let it warm up to room temperature for 20 minutes.

[0059] 1.2.2 Blocking: Wet the incubation tank with distilled water, place the chip protein side up in the tank, add 5 mL of blocking solution (5% bovine serum albumin phosphate buffer solution), gently shake to cover the chip protein side, and cover the tank. Place the tank on a shaker and shake slowly horizontally at a frequency of 70 times / min, and incubate at room temperature for 1.5 h.

[0060] 1.2.3 Sample preparation: Take out the patient's serum sample from -80℃ in advance, thaw it in an ice bath or at room temperature, shake it to mix well, and centrifuge it at high speed (12000rpm) for 10min; take 5μL of the supernatant and mix it with 5mL of blocking solution at a ratio of 1:1000 by vortexing.

[0061] 1.2.4 Sample addition: Discard the sealing solution in the incubation tank, add the sample prepared in 1.2.3 directly, and gently shake to cover the chip; cover the tank and incubate at room temperature for 1 hour.

[0062] 1.2.5 Rinsing: Wash 6 times with PBST buffer. For the last 3 rinsing processes, place the container on a shaker and shake it rapidly from side to side for 10 minutes each time, with a reciprocating frequency of 100 times / min.

[0063] 1.2.6 Secondary antibody preparation: Turn off the lights and remove the blocking buffer and secondary antibody IgG from the freezer 10 minutes in advance. Take a 5mL cryovial, wrap it with aluminum foil, add 10μL of secondary antibody IgG and 5mL of blocking buffer, and vortex to mix.

[0064] 1.2.7 Adding secondary antibody: After rinsing, add 5 mL of secondary antibody to the incubation tank and gently shake to ensure it is completely submerged; cover the tank and wrap it with aluminum foil to protect it from light. Place it on a shaker and gently shake horizontally at a frequency of 70 times / min, incubate at room temperature for 1 hour.

[0065] 1.2.8 Rinsing: Wash 6 times with PBST buffer first. For the last 3 rinses, place the container on a shaker and shake rapidly from side to side for 10 minutes each time. Then wash 3 times with 10× PBST buffer, placing the container on a shaker and shaking rapidly from side to side for 10 minutes each time.

[0066] 1.2.9 Drying the chips: After rinsing, remove the chips from the incubation tank, hang them vertically on absorbent paper to drain, and place them in the chip box.

[0067] 1.2.10 Scanning and image reading:

[0068] 1.2.10.1 Warm up the device 20 minutes in advance: Turn on the computer power button, host switch, and scanner switch; open the Genepix pro-Device software.

[0069] 1.2.10.2 Place the chip with the protein side of the barcode facing down—set the laser excitation to 635nm (power = 95, photomultiplier tube = 700) for scanning—click the green triangle start button to begin scanning—after scanning, click the white envelope, the third bar, and save in .tif format.

[0070] 1.2.11 Data Extraction / Marking:

[0071] Open Genepix software – drag the tif and gal files together into a small window – settings (top left image and bottom right blue settings PMT-GAN) – data processing: select the region, adjust the region's position based on fixed positive points (two adjacent points with strong signals are considered positive). There's no need to drag false positive points to the black background area; only operate to ensure that the quasi-positive points (intensity approximately 10 times that of the background) are precisely enclosed (move up, down, left, and right; use Ctrl+up and down to adjust the size) – save as a GPS file, with the same name as the tif file.

[0072] 1.3 Experimental Results

[0073] All raw intensity data obtained in section 1.2.11 were normalized by Loess and transformed by log2 before differential analysis. Differential antibodies between the relapse and non-relapse groups were selected using the R software limma (version 4.1.1). Using P ≤ 0.05, 1036 differential antibodies were identified in 11 patients with relapsed DLBCL and 9 patients without relapsed DLBCL. Using / FC / ≥ 1.5 and P ≤ 0.05 as cutoff values ​​for candidate autoantibodies for the microarray, the top 200 autoantibodies were selected for custom microarray design (see Table 1).

[0074] Table 1. Results of autoantibody screening for large-chip microarrays

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Example 2: Microchip validation of the specificity and sensitivity of autoantibodies in the prognosis of DLBCL

[0082] 181 DLBCL patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) were randomly selected and grouped according to whether they relapsed within 2 years (event-free 24 months, EFS24): 124 non-relapsed patients and 57 relapsed patients. Univariate Cox regression analysis was performed on the microarray data, followed by multivariate Cox regression analysis with IPI score correction to identify autoantibodies that could predict both PFS (progression-free survival: the time from the start of treatment to the observation of disease progression or death from any cause) and OS (overall survival: the time from randomization to death from any cause). Lasso-Cox regression analysis was then performed on PFS and OS separately, and EFS24 group difference analysis was conducted. The intersection was taken to obtain autoantibodies that could predict both PFS and OS and distinguish between different EFS24 groups. The specific experimental procedure is as follows:

[0083] 2.1 Fabrication of small chips

[0084] The small chip fabrication process involves expressing a protein with a GST tag using a yeast expression system, and then dividing it into 2×7 subarrays using a 14-chamber rubber gasket (GraceBio Corp, Bend, OR).

[0085] 2.2 Verification of the small chip:

[0086] 2.2.1 Preservation of the protein chip: Store the plastic chip cartridge containing the DLBCL targeted protein chip at -80°C. After removing the chip cartridge, place it in PE gloves and leave it at room temperature for 20 minutes.

[0087] 2.2.2 Install the fence: With the chip face up, attach the matching chip fence to the chip according to the position of each array on the chip, and secure it to ensure that the fence is completely attached to the chip. Then place it in the incubation box.

[0088] 2.2.3 Blocking: Add 60 μL of blocking solution (3% BSA (w / v), PBS-T) to each array of the chip, incubate at room temperature for 1 hour, and gently shake on a shaker (60 rpm).

[0089] 2.2.4 Sample preparation: Take out the serum in advance, dissolve it, centrifuge at 12000 rpm for 10 minutes, dilute the serum with blocking buffer (3% BSA (w / v), PBS-T) at a ratio of 1:2000, mix well, and serial dilution is recommended.

[0090] 2.2.5 Hybridization: Discard the blocking solution from the chip array, and carefully add the prepared serum sample (60 μL) to each array, avoiding the formation of air bubbles. Incubate at room temperature for 1 hour.

[0091] 2.2.6 Washing: After incubation, discard the liquid in the chip array, remove the baffles, and place the chips into the incubation chamber. Add 10.0 mL of PBS-T to each cell, and wash by shaking at room temperature for 10 minutes (40 rpm). Repeat three times.

[0092] 2.2.7 Drying the chip: Use flat-tipped tweezers to remove the chip from the incubation box and place it vertically in a 50mL centrifuge tube. Centrifuge at 1000rpm for 2 minutes. Place absorbent paper on the work surface, remove the chip from the centrifuge tube, and place it vertically in contact with the absorbent paper to absorb any remaining moisture from the chip's edges.

[0093] 2.2.8 Install fences and add fluorescently labeled secondary antibody for incubation: Install fences according to the position of each array on the chip, ensuring proper fit. Dilute the secondary antibody (Alexa 647-labeled goat anti-human IgG antibody) with 3% BSA blocking buffer at a ratio of 1:2000, vortex to mix, add 60 μL of the diluted secondary antibody to each array, and incubate at room temperature for 1 hour, protected from light.

[0094] 2.2.9 Washing: After incubation, discard the liquid in the chip array, remove the enclosure, and place the chips into the incubation chamber. Add 10.0 mL of PBS-T to each cell, and gently shake at room temperature for 10 minutes (40 rpm). Repeat three times, protecting from light. Rinse the protein chips with 10 mL of ddH2O for 10 minutes each time, repeating three times.

[0095] 2.2.10 Drying the Slides: Using flat-tipped forceps, remove the chip from the incubation box and place it vertically in a 50mL centrifuge tube. Centrifuge at 1000rpm for 2 minutes. Place absorbent paper on the work surface, remove the chip from the centrifuge tube, and place it vertically in contact with the absorbent paper to absorb any remaining moisture from the chip's edges. Transfer the dried chip to a new, clean slide container.

[0096] 2.2.11 Scanning and Saving Data

[0097] The specific process is the same as steps 1.2.10 and 1.2.11 of Example 1.

[0098] 2.3 Experimental Results

[0099] After screening autoantibodies using univariate Cox regression statistical tests for PFS and OS in all 181 patients obtained in section 2.2.11, multivariate Cox regression-corrected IPI score statistical tests were used to screen out 13 antibodies with overlapping PFS and OS (see [link to relevant documentation]). Figure 1 , Figure 2 Lasso-Cox regression tests were performed on PFS and OS for the 13 autoantibodies (see [link to relevant documentation]). Figure 3 , Figure 4 Six antibodies (anti-HIST2H3A, anti-PRKAA1, anti-DEAF1, anti-N4BP1, anti-CREB1, and anti-UBAP2) were selected. The Mann-Whitney U test showed statistically significant differences in the nine antibodies (anti-TNFRSF10D, anti-N4BP1, anti-RBM33, anti-CYP4F8, anti-CT55, anti-ZNF398, anti-DEAF1, anti-CREB1, and anti-UBAP2) between 57 patients with relapsed DLBCL and 124 patients with non-relapsed DLBCL (P≤0.01) (see [link to study]). Figure 5 Based on the combined results of multivariate Cox regression statistical test, Loss-Cox regression test, and Mann-Whitney U test, anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1 were selected for ELISA detection.

[0100] Example 3: ELISA validation of the specificity and sensitivity of autoantibodies in the prognosis of DLBCL

[0101] 135 DLBCL patients who received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and had baseline blood samples before treatment were randomly selected and retrospectively grouped according to EFS24 (whether there was relapse within two years): 102 non-relapsed patients and 33 relapsed patients. Anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1 levels in the serum of the 135 patients were detected using an ELISA kit. The Man Whitney U test (rank-sum test) was performed in the 135 cohorts to distinguish between the good response (no relapse within two years) and poor response (relapse within two years) groups. Kaplan-Meier survival curve analysis was used to analyze PFS and OS. The specific experimental procedure is as follows:

[0102] 3.1 ELISA Steps

[0103] 3.1.1 Protein coating:

[0104] 3.1.1.1 Preparation of coating solution: Weigh 1.59g of Na2CO3 and 2.93g of NaHCO3, dissolve them in 900mL of deionized water, adjust the pH to 9.6, add water to 1L, and store at 4℃.

[0105] 3.1.1.2. Take out the target protein (N4BP1, CREB1, UBAP2 or DEAF1), thaw it on ice, and dilute it to 1 μg / mL with coating buffer.

[0106] 3.1.1.3 Add 50 μL of diluted protein to each well, seal the plate, and incubate overnight at 4°C.

[0107] 3.1.2, Closed

[0108] 3.1.2.1 Prepare at least 30 mL of 5% milk in advance using PBST and place it at 4°C, and place 500 mL of PBST at room temperature.

[0109] 3.1.2.2 Remove the coated plate, discard the coating solution, and wash with PBST 5 times for 3 minutes each time.

[0110] 3.1.2.3 Add 50 μL of 5% milk to each well, seal the plate, and incubate at room temperature for 2 hours.

[0111] 3.1.3 Plasma dilution

[0112] 3.1.3.1 Half an hour before the sealing is completed, take out the plasma sample, thaw it on ice for 20 minutes, and briefly separate it from the plasma before opening the cap.

[0113] 3.1.3.2 Dilute the plasma with 5% milk to 1:100, 1:300, and 1:600, mix well, and store at 4°C. (Based on preliminary experimental results, the primary antibody concentration was set at 1:300.)

[0114] 3.1.4. Incubate primary antibody (with plasma)

[0115] 3.1.4.1 Discard the milk in the plate, wash 5 times with PBST for 5 minutes each time, and pat the liquid in the plate dry on paper after the last wash.

[0116] 3.1.4.2. Add 50 μL of diluted plasma to each well, 50 μL of 5% milk to each negative control well, and anti-GST protein-tagged antibody to each positive control well. Detect anti-N4BP1, anti-CREB1, anti-UBAP2, and anti-DEAF1, respectively. Seal the plate and incubate on a shaker at room temperature for 1 hour.

[0117] 3.1.5. Add secondary antibody

[0118] 3.1.5.1 Dilution of secondary antibody: Take out the secondary antibody and dilute it with 5% milk to determine a dilution concentration of 1:8000.

[0119] 3.1.5.2 After the primary antibody incubation is complete, discard the primary antibody liquid in the plate, wash 5 times with PBST for 5 minutes each time, and after the last wash, pat the liquid in the plate dry on paper.

[0120] 3.1.5.3 Add 50 μL of diluted HRP-labeled secondary antibody to each well, seal the plate, and incubate on a shaker at room temperature for 1 h.

[0121] 3.1.6 Color Development

[0122] 3.1.6.1 After the secondary antibody incubation begins, take out an appropriate amount of TMB and place it in a dark place at room temperature.

[0123] 3.1.6.2 After the secondary antibody incubation is completed, discard the secondary antibody liquid in the plate, wash 5 times with PBST for 5 minutes each time, and pat the liquid in the plate dry on paper after the last wash.

[0124] 3.1.6.3 Add 100 μL of TMB to each well. Start timing when the TMB is completely added.

[0125] 3.1.6.4. Place at room temperature for 25-30 minutes, depending on when the positive well turns blue.

[0126] 3.1.6.5. Add 50 μL of 0.5 M sulfuric acid to each well to terminate the colorimetric reaction.

[0127] 3.1.7 Detection

[0128] Detect immediately after adding sulfuric acid to stop the reaction, and after a brief shake, detect at a wavelength of 450 nm.

[0129] 3.2 Experimental Results

[0130] The ELISA test results of 135 patients were statistically analyzed, and the results are shown in Table 2.

[0131] Table 2. Autoantibody levels in 135 patients

[0132]

[0133]

[0134]

[0135]

[0136] The data in Table 2 were analyzed using the "maxstat" package in R. The optimal cutoff value was calculated based on the `maxstat.text` function, which performs maximum selected rank statistics. After determining the optimal cutoff value for each autoantibody, the 135 patients were divided into a high antibody level group (high IgG) and a low antibody level group (low IgG). Kaplan-Meier curves for progression-free survival (PFS) and overall survival (OS) (based on the cutoff value calculated from PFS) were plotted. The OD values ​​for the four autoantibodies were defined as follows: anti-UBAP2 > 1.37, anti-DEAF1 > 0.789, anti-CREB1 > 0.89, and anti-N4BP1 > 1.31, indicating high antibody levels. See [link to results]. Figures 6-9 .

[0137] In addition, patients from the 135 cohorts were grouped according to EFS24, and the AUC of autoantibodies or antibody combinations was calculated. The results are shown in [link to results]. Figure 10 And Table 3.

[0138] Table 3. AUC of four autoantibodies and their combinations

[0139]

[0140] Note: + indicates the type of antibody present in the antibody combination.

[0141] Figures 6-9 The results showed that the differences in the levels of the four antibodies between the relapse and non-relapse groups in EFS24 were statistically significant (P<0.05). Kaplan-Meier curve analysis indicated that all four antibodies in baseline serum could predict PFS and OS in 135 DLBCL patients; higher antibody levels detected by ELISA in baseline serum before treatment were associated with longer PFS and OS (P<0.05). The differences between the relapse and non-relapse groups were statistically significant (P<0.05). Figure 10 The results in Table 3 show that the AUCs of the four autoantibodies in distinguishing EFS24 ranged from 0.69 to 0.73. The AUCs of different autoantibody combinations in distinguishing EFS24 ranged from 0.725 to 0.742, which were significantly higher than the AUCs of individual autoantibodies. Among them, the anti-CREB1 + anti-N4BP1 combination had the highest AUC value, reaching 0.742.

[0142] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

Claims

1. The use of an autoantibody detection reagent in the preparation of a kit for assessing the prognosis of drug treatment for diffuse large B-cell lymphoma, wherein the autoantibody includes anti-CREB1.

2. The application according to claim 1, wherein the kit further comprises at least one of the following: a detection reagent for anti-N4BP1 autoantibody, a detection reagent for anti-DEAF1 autoantibody, and a detection reagent for anti-UBAP2 autoantibody.

3. The application according to claim 1, wherein the kit further comprises: Detection reagent for autoantibody anti-N4BP1; Reagent for detecting autoantibody anti-DEAF1; Reagent for detecting autoantibody anti-UBAP2; Reagents for detecting autoantibody anti-N4BP1 and autoantibody anti-DEAF1; Reagents for detecting autoantibody anti-N4BP1 and autoantibody anti-UBAP2; or Reagents for detecting autoantibodies anti-N4BP1, anti-DEAF1, and anti-UBAP2.

4. The application according to any one of claims 1 to 3, wherein the autoantibody is derived from autoantibodies in peripheral blood.

5. The application according to any one of claims 1 to 3, wherein the application includes detecting the expression level of autoantibodies to assess the prognosis of drug treatment for diffuse large B-cell lymphoma.

6. The application according to any one of claims 1 to 3, wherein the detection reagent for the autoantibody comprises reagents for protein chips and / or ELISA.

7. The application according to any one of claims 1 to 3, wherein the drug treatment comprises anti-CD20 antibody drug treatment.

8. The application according to claim 6, wherein the OD of the autoantibody in the subject is detected using an ELISA method. 450 Value, when anti-N4BP1 OD 450 >1.31、Anti-CREB1 OD 450 >0.89, Anti-UBAP2 OD 450 >1.37 and / or anti-DEAF1OD 450 When the value is >0.789, the prognosis for the subject with drug treatment is good.

9. A system for evaluating the prognosis of drug treatment for diffuse large B-cell lymphoma, wherein the system comprises: An acquisition module, wherein the acquisition module is used to acquire samples from the subject; An evaluation module, connected to an acquisition module, includes a kit for evaluating the prognosis of drug treatment for diffuse large B-cell lymphoma and for detecting autoantibodies in samples. The kit contains detection reagents for autoantibodies, including anti-CREB1.

10. The system of claim 9, wherein the kit further comprises at least one of the following: a detection reagent for anti-N4BP1 autoantibody, a detection reagent for anti-DEAF1 autoantibody, and a detection reagent for anti-UBAP2 autoantibody.

11. The system of claim 9, wherein the kit further comprises: Detection reagent for autoantibody anti-N4BP1; Reagent for detecting autoantibody anti-DEAF1; Reagent for detecting autoantibody anti-UBAP2; Reagents for detecting autoantibody anti-N4BP1 and autoantibody anti-DEAF1; Reagents for detecting autoantibody anti-N4BP1 and autoantibody anti-UBAP2; or Reagents for detecting autoantibodies anti-N4BP1, anti-DEAF1, and anti-UBAP2.

12. The system according to any one of claims 9-11, characterized in that: The system also includes an output module, which outputs results based on the detection data from the evaluation module.

13. The system according to any one of claims 9-11, wherein the evaluation module includes assessing the prognosis of drug treatment for diffuse large B-cell lymphoma by detecting the expression level of autoantibodies.

14. The system according to any one of claims 9-11, wherein the detection reagent for the autoantibody comprises reagents for protein chips and / or ELISA.

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