Use of reagents for detecting biomarkers in the manufacture of a product for predicting disease activity in ulcerative colitis or a diagnostic product for ulcerative colitis

Abstract

The application discloses application of a reagent for detecting a biomarker in preparation of a product for predicting ulcerative colitis disease activity or a product for diagnosing ulcerative colitis disease, and relates to the technical field of serological markers of inflammatory bowel disease. The serum CD105 of a UC patient is significantly higher than that of a healthy control group, and CD105 has a very high effect of diagnosing ulcerative colitis disease, and CD105 can distinguish disease activity from remission. Therefore, the application provides a novel serum marker which is convenient to sample, has good repeatability and can objectively reflect the colon mucosa inflammation load, and is expected to provide more accurate marker selection for UC disease activity prediction and UC diagnosis.

Description

Technical Field

[0001] This invention relates to the field of serological biomarkers for inflammatory bowel disease (IBD), and more specifically, to the use of reagents for detecting biomarkers in the preparation of products for predicting the activity of ulcerative colitis or for the diagnosis of ulcerative colitis. Background Technology

[0002] Ulcerative colitis is a chronic, relapsing, nonspecific inflammation of the colorectal mucosa, characterized by alternating periods of activity and remission. Accurate and non-invasive assessment of mucosal inflammation activity is crucial for guiding treatment, reducing complications, and lowering the risk of colorectal cancer. Currently, commonly used clinical methods for assessing activity include: a) Endoscopic examination—Mayo endoscopy score: accurate results, but highly invasive, expensive, poor patient compliance, and not suitable for frequent repetition; b) Clinical scoring systems—such as the Mayo Clinical Score and partial Mayo scores: are greatly influenced by subjective symptoms and have only 60%–70% consistency with endoscopic mucosal healing. c) Fecal biomarkers—fecal calprotectin (FC) and fecal lactoferrin: Although non-invasive, they are easily affected by gut microbiota, diet, and drugs (such as proton pump inhibitors), and can also be elevated by upper gastrointestinal inflammation, so their specificity is limited. d) Serological markers—C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR): The positive rate in UC patients is only 50%–60%, and they are affected by multiple factors such as infection, obesity, and metabolic syndrome, and have low sensitivity in mild to moderate active phases.

[0003] Therefore, there is still a lack of a novel serum biomarker in this field that is convenient to sample, has good repeatability, and can objectively reflect the inflammatory burden of the colonic mucosa.

[0004] Endoglin (CD105) is a transmembrane glycoprotein primarily expressed on vascular endothelial cells, activated monocytes / macrophages, and placental syncytiotrophoblast cells. It participates in the TGF-β superfamily signaling pathway, regulating angiogenesis and immune tolerance. Its extracellular domain is cleaved by metalloproteinases to generate a soluble form—secreted endoglin (sENG). Previous reports have shown that sENG is elevated in preeclampsia, atherosclerosis, and various solid tumors, and is correlated with the degree of vascular endothelial injury. However, to date, no studies have systematically evaluated the expression characteristics of sENG in the peripheral blood of patients with ulcerative colitis, nor have they reported its use as a predictor of UC disease activity.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide reagents for detecting biomarkers for use in the preparation of products for predicting the activity of ulcerative colitis or for the diagnosis of ulcerative colitis, thereby objectively reflecting the inflammatory load of the colonic mucosa.

[0007] This invention is implemented as follows: This invention provides an application of a reagent for detecting biomarkers in the preparation of products for predicting the activity of ulcerative colitis or in the preparation of diagnostic products for ulcerative colitis. The biomarker is secretory endothelial glycoprotein, including CD105.

[0008] The present invention has the following beneficial effects: This invention, through screening, found that serum CD105 levels in ulcerative colitis (UC) patients were significantly higher than in healthy controls. Furthermore, the diagnostic efficacy of CD105 for UC was assessed, demonstrating extremely high efficacy in diagnosing ulcerative colitis. CD105 also exhibits good clinical efficacy as a differential indicator in distinguishing between disease activity and remission. Therefore, this invention provides a novel serum biomarker that is convenient to sample, has good repeatability, and objectively reflects the inflammatory burden of the colonic mucosa, potentially offering more accurate biomarker selection for predicting UC disease activity and UC diagnosis. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 Figure A shows the comparison of serum sENG levels between UC patients and healthy controls in cohort 1, and Figure B shows the ROC curve analysis results (***p<0.001). Figure 2 A graph showing the results of serum sENG predicting clinical disease activity in UC patients; based on the Mayo Criterion score, 210 UC patients were divided into: remission (total score ≤ 2, n=72) and active (total score > 2, n=138); (A) Comparison of serum sENG levels between remission and active UC patients; (B) ROC curve analysis of the efficacy of serum sENG in differentiating between remission and active UC; ***p<0.001; Data source: UC cohort 1 and control cohort 1; Figure 3A graph showing the results of serum sENG predicting the activity of colonic mucosal inflammation in UC patients; based on the Mayo Criterion score, 210 UC patients were divided into: remission phase (total score ≤ 1 point, n=82) and active phase (total score > 1 point, n=128); (A) Comparison of serum sENG levels between remission and active phases of UC patients; (B) ROC curve analysis of the efficacy of serum sENG in differentiating between remission and active phases of colonic mucosal inflammation in UC; ***p<0.001. Data source: UC cohort 1 and control cohort 1; Figure 4 The results of CRP efficacy in predicting disease activity in UC patients are shown in the graphs. (A) ROC curve analysis shows the efficacy of CRP in differentiating between clinical remission and active phases of UC. (B) ROC curve analysis shows the efficacy of CRP in differentiating between endoscopic remission and active phases of colonic mucosal inflammation in UC. Data source: UC cohort 1 and control cohort 1. Figure 5 ROC curve validation analysis results for the efficacy of serum sENG in differentiating UC from controls (A) and predicting disease activity in UC patients (B and C). Data source: UC cohort 2 and control cohort 2. Figure 6 A graph showing the efficacy of CRP in predicting disease activity in UC patients (A represents clinical disease activity, B represents endoscopic mucosal inflammation activity); data source: UC cohort 2. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0012] Definitions: Ulcerative colitis disease activity refers to the dynamic and quantifiable clinical status that reflects the intestinal mucosal inflammatory activity, lesion extent, and systemic response intensity of patients with ulcerative colitis. Essentially, it is the macroscopic manifestation of disease biological activity (such as the release of pro-inflammatory factors, neutrophil infiltration, and epithelial barrier disruption) and requires objective assessment using standardized scoring tools.

[0013] The term "marker" broadly refers to any detectable compound or cell present in or derived from a sample, such as a protein, peptide, proteoglycan, glycoprotein, lipoprotein, cell, or any of the foregoing substances, that is differentiating molecule or differentiating fragment. For example, the detection of or binding to a specific antibody can indicate the presence of a specific antigen (e.g., a protein) in a sample. Here, a differentiating molecule or fragment is a molecule or fragment that, upon detection, indicates the presence or abundance of the aforementioned identified compound or cell. Markers can, for example, be isolated from the sample, measured directly in the sample, or detected or determined in the sample. Markers can, for example, be functional, partially functional, or non-functional. Markers may also be synonymous with "biomarker."

[0014] The term "sample" refers to a biological specimen obtained from or derived from an individual for a purpose. The source of the biological specimen can be a fresh, frozen, and / or preserved organ or tissue sample, or solid tissue derived from a biopsy or primers; blood or any blood component. The term "sample" includes biological samples that have been manipulated in any way after acquisition, such as by reagent treatment, stabilization, enrichment for certain components (e.g., proteins or polynucleotides), or embedding in a semi-solid or solid matrix for sectioning purposes. In this invention, the sample is particularly a peripheral blood sample or a serum sample.

[0015] As used herein, the term "subject" can be understood as anyone involved in the diagnosis or auxiliary diagnosis of inflammatory bowel disease (IBD). A subject can be a patient in a clinical setting undergoing one or more tasks related to the diagnosis of IBD, preparing the patient for one or more tests for IBD. IBD specifically refers to ulcerative colitis (UC).

[0016] The term "area under the curve" or "AUC" refers to the area under the receiver operating characteristic (ROC) curve, both of which are well-known in the field. AUC measurements are useful for comparing the accuracy of classifiers across the entire data range. Classifiers with higher AUCs have a greater ability to correctly classify unknown data between two target groups (e.g., a sample of ulcerative colitis (UC) and a healthy group without UC, or a clinically active group versus a remission group, or a mucosal inflammation active group versus a remission group). ROC curves are useful for depicting the performance of a specific feature (e.g., any biomarkers and / or any entries of additional biomedical information described in this invention) when distinguishing between two populations (e.g., individuals in the ulcerative colitis (UC) group and a healthy group without UC). Typically, feature data are selected across the entire population (e.g., cases and controls) in ascending order based on the values ​​of a single feature. Then, for each value of that feature, the true positive and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases with values ​​higher than the characteristic and dividing by the total number of cases. The false positive rate is determined by counting the number of controls with values ​​higher than the characteristic and dividing by the total number of controls. While this definition refers to cases where the characteristic is higher than the control, it also applies to cases where the characteristic is lower than the control (in which case samples with values ​​lower than the characteristic will be counted).

[0017] The term "predetermined threshold" refers to a parameter used to compare a marker or combination of markers in a subject's sample with a predetermined threshold when diagnosing disease risk, and outputs the subject's disease risk or disease course based on the comparison result.

[0018] Specifically, the predetermined threshold includes, but is not limited to, critical values ​​that can be used to classify results and clarify judgment boundaries, such as positive judgment values, predicted probability thresholds, and dependent variable thresholds. The setting of this threshold needs to be based on the performance requirements of the target detection scenario (such as sensitivity, specificity, and accuracy), and determined through clinical data validation, statistical model analysis (such as ROC curve analysis combined with Youden's index calculation), or industry standard calibration, to ensure its validity and reliability in the detection method or diagnostic model. The predetermined threshold of this invention is preferably set as a cut-off value.

[0019] In a first aspect, the present invention provides an application of a reagent for detecting a biomarker in the preparation of a product for predicting the activity of ulcerative colitis or in the preparation of a diagnostic product for ulcerative colitis, wherein the biomarker is a secretory endothelial glycoprotein, including CD105.

[0020] Screening revealed that serum CD105 levels in UC patients were significantly higher than in healthy controls. The diagnostic efficacy of CD105 for UC was assessed, demonstrating extremely high efficacy in diagnosing ulcerative colitis. CD105 also showed good clinical efficacy as a differential indicator in distinguishing between disease activity and remission. Therefore, this invention provides a novel serum biomarker that is convenient to sample, has good repeatability, and can objectively reflect the inflammatory burden of the colonic mucosa, potentially offering more accurate biomarker selection for predicting UC disease activity and UC diagnosis.

[0021] soluble Endoglin (sENG) is a secreted endothelial glycoprotein.

[0022] In a preferred embodiment of the present invention, the biomarker is derived from peripheral blood, body fluids, or umbilical cord blood. Body fluids include, but are not limited to, intracellular fluid, urine, tears, sweat, etc.

[0023] In a preferred embodiment of the present invention, the reagent for detecting biomarkers is a reagent for detecting the protein level of biomarkers.

[0024] In a preferred embodiment of this invention, the biomarker reagent is one that detects the level of the biomarker protein in the sample using ELISA, Western blotting, and / or Elisopt methods. Any reagent capable of detecting the aforementioned CD105 level is within the scope of protection of this invention.

[0025] In a preferred embodiment of the present invention, the product for predicting the activity of ulcerative colitis or the diagnostic product for ulcerative colitis is selected from at least one of reagents, test strips, kits, or chips.

[0026] In a preferred embodiment of the present invention, the kit is selected from ELISA kits, immunoturbidimetric kits, or flow cytometry kits.

[0027] In a preferred embodiment of the present invention, the application includes at least one of the following application methods: (1) Based on the characteristic data of biomarkers contained in the subjects' test samples, generate the predicted results of the subjects' ulcerative colitis disease activity; (2) Based on the characteristic data of biomarkers contained in the subject's sample, generate the subject's ulcerative colitis diagnosis result.

[0028] In a preferred embodiment of the present invention, based on the characteristic data of biomarkers contained in the subject's test sample, a clinical activity prediction result for the subject's ulcerative colitis is generated, and / or, an endoscopic mucosal inflammation activity prediction result for the subject's ulcerative colitis is generated.

[0029] By generating clinical activity predictions for ulcerative colitis in subjects, it is possible to assess whether subjects have experienced clinical symptom relief.

[0030] By generating endoscopic mucosal inflammation activity prediction results for subjects with ulcerative colitis, it is possible to assess whether the subjects have experienced any relief of endoscopic mucosal inflammation.

[0031] In one implementation, both results can be generated simultaneously, or either result can be generated.

[0032] In a preferred embodiment of the present invention, based on a comparison of the concentration of biomarkers contained in the subject's sample with a predetermined threshold, a predicted result of the subject's ulcerative colitis activity or a diagnostic result of ulcerative colitis is generated according to the comparison result. The predetermined threshold is either the threshold obtained from the directly generated ROC curve, or the threshold obtained from the ROC curve generated based on the machine learning model.

[0033] In a preferred embodiment of the present invention, the machine learning model is selected from at least one of the following: logistic regression, random forest, K-nearest neighbors, Gaussian Bayes, Naive Bayes, support vector machine, AdaBoost, XGBoost, and DT decision tree.

[0034] Secondly, the present invention also provides a device for predicting the activity of ulcerative colitis or a diagnostic device for ulcerative colitis. The device includes a processor and a memory, the memory storing a set of executable program instructions, which are loaded and executed by the processor to implement the above-mentioned method for predicting the activity of ulcerative colitis or the method for diagnosing ulcerative colitis.

[0035] Specifically, the electronic device may include a memory, a processor, a bus, and a communication interface, which are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components may be electrically connected to each other via one or more buses or signal lines. The processor may process information and / or data related to target identification to perform one or more functions described in this application.

[0036] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0037] A processor can be an integrated circuit chip with signal processing capabilities. This processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0038] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0039] Example 1 This embodiment screens and validates serum sENG (CD105) biomarkers.

[0040] (1) Sample inclusion This project included two cohorts of patients diagnosed with UC and two healthy control cohorts: UC cohort 1 (n = 210) and control cohort 1 (n = 157), with data used for marker screening; and UC cohort 2 (n = 100) and control cohort 2 (n = 100), with data used for marker validation.

[0041] The diagnosis of UC was based on a comprehensive analysis of medical history, clinical presentation, radiological, endoscopic and histological examinations, and laboratory tests. UC patients and controls were matched for sex and age. The Mayo Criterion (total score of 9, without endoscopic scoring) was used to determine the disease activity in UC patients. The extent of UC lesions was determined according to the Montreal Classification.

[0042] The Mayo Criterion System (total score of 9, no endoscopic scoring items) standards are as follows:

[0043] The Mayo Criterion (MCC) score is graded as follows: Total score ≤ 2 points, clinical remission; 3 ≤ Total score < 5 points, mild activity; 5 ≤ Total score < 7 points, moderate activity; Total score ≥ 7 points, severe activity.

[0044] Specific criteria for Mayo endoscopic scoring:

[0045] The Montreal classification criteria are as follows:

[0046] The exclusion criteria for inclusion in UC patients and the control group were as follows: smoking, alcoholism, hematopoietic system diseases, hepatobiliary diseases, coagulation abnormalities, use of drugs that may affect blood cell components or serum biochemical characteristics, hypertension, diabetes, infection, other systemic autoimmune diseases, other gastrointestinal diseases, and cancer.

[0047] 2. Serum sENG concentration detection Enzyme-linked immunosorbent assay (ELISA) was used. The sENG detection kit was purchased from R&D Systems, Inc. (Minneapolis, MN, USA). Detailed operating procedures are as follows: (1) Antigen coating: Dilute the antigen to 10 μg / ml with carbonate coating solution, add 100 μl to each well, and incubate overnight at 4°C. Aspirate the antigen liquid and wash each well with 100 μl of PBST for 5 min.

[0048] (2) Sealing: Take 100 ml of each sample. Add blocking buffer (5% BSA) to each well and incubate at room temperature for 60 min. Remove the blocking buffer and wash with PBST (100 μl for 5 min). One wash is sufficient.

[0049] (3) Primary antibody incubation: Remove the washing solution, add 100 μl of primary antibody diluted 500-1000 times with 5% BSA to each well, and incubate at room temperature for 1 h.

[0050] (4) Washing: Remove the liquid from the enzyme-labeled wells, wash each well three times with PBST, with a volume of 100 μl per well and a washing time of 5 min each time.

[0051] (5) Add enzyme-labeled antibody: Aspirate the washing solution and add 100 μl of secondary antibody diluted with 5% BSA at 1:2000 to each well. Incubate at room temperature for 1 h.

[0052] (6) Washing: Remove the liquid in the wells, wash three times with PBST for 5 min each time, with a volume of 100 μl per well, and then add 100 μl of PBS to each well for 5 min each time.

[0053] (7) Color development: Add 100 μl of TMB substrate solution to each well. Keep out of light during the addition process and react at room temperature for 40 min in the dark.

[0054] (8) Termination of reaction: Add 100 μl of 2M sulfuric acid to each enzyme-labeled well to terminate the reaction.

[0055] (9) Reading the plate: Place the ELISA plate in the ELISA reader and read the value.

[0056] 3. Results Analysis (1) In cohort 1 of UC and cohort 1 of control, the level of sENG in peripheral venous serum was analyzed, and it was found that the serum sENG level in UC patients was significantly higher than that in the healthy control group (e.g., Figure 1 (As shown in A).

[0057] Next, sENG was used to assess its diagnostic efficacy for UC. The area under the ROC curve (AUC) was 0.7854 (p<0.001) (as shown in B of Figure 1). When the cut-off value (i.e., the preset threshold) was set to 4.46 ng / mL, the sensitivity of sENG in identifying UC was 79.43%, and the specificity was 82.34%.

[0058] (2) Using the Mayo Criterion System, UC patients were further divided into an active clinical phase group and a remission phase group based on the clinical activity of the disease. First, as Figure 2 As shown in Figure A, serum sENG levels were significantly higher in UC patients in the clinical active phase than in UC patients in the remission phase. ROC curve analysis indicated an area under the curve (AUC) of 0.7647 (p<0.001). Figure 2 (As shown in B).

[0059] (3) Further, using the Mayo Clinic endoscopic scoring system, the included UC patients were further divided into an active mucosal inflammation group and a remission group based on the degree of conjunctival mucosal inflammation activity observed endoscopically. First, such as Figure 3 As shown in Figure A, serum sENG levels in UC patients in the active phase of colonic mucosal inflammation were significantly higher than those in the remission phase. ROC curve analysis indicated an area under the curve (AUC) of 0.8083 (p<0.001). Figure 3 (As shown in B).

[0060] When the cut-off value (i.e., the preset threshold) was set to 6.18 ng / mL, the sensitivity of sENG in judging active UC was 78.13% and the specificity was 74.55%, suggesting that serum sENG, as an alternative tool for endoscopy, has good clinical efficacy in differentiating between active and remission colonic mucosal inflammation.

[0061] Example 2 The diagnostic efficacy of UC disease was evaluated using UC cohort 2 from Example 1 and control cohort 2.

[0062] Specifically, to further validate the diagnostic efficacy of sENG for ulcerative colitis (UC), we included UC cohort 2 (n = 100) and control cohort 2 (n = 100). In UC cohort 2, there were 67 patients in the clinical active phase and 33 patients in the clinical remission phase; 65 patients in the active phase of endoscopic mucosal inflammation and 35 patients in the remission phase of endoscopic mucosal inflammation.

[0063] Diagnostic efficacy of sENG in differentiating UC patients from controls: Area under the ROC curve (AUC) was 0.8622 (p<0.001). Figure 5 (As shown in Figure A). When the cut-off value (i.e., the preset threshold) is set to 4.46 ng / mL, the sensitivity of sENG in determining UC is 79% and the specificity is 85%.

[0064] In identifying clinically active ulcerative colitis (UC), the diagnostic efficacy of sENG was: area under the ROC curve (AUC) was 0.7820 (p<0.001). Figure 5 (As shown in Figure B). When the cut-off value (i.e., the preset threshold) is set to 6.98 ng / mL, the sensitivity of sENG in determining UC is 78.13% and the specificity is 75.76%.

[0065] In identifying active ulcerative colitis (UC) of the mucosa under endoscopic visualization, the diagnostic efficacy of sENG was: area under the ROC curve (AUC) was 0.8040 (p<0.001). Figure 5 (As shown in C). When the cut-off value (i.e., the preset threshold) is set to 7.06 ng / mL, the sensitivity of sENG in determining UC is 75.85% and the specificity is 77.14%.

[0066] Example 3 This embodiment provides a device for predicting the activity of ulcerative colitis. The device includes a processor and a memory. The memory stores a set of executable program instructions, which are loaded and executed by the processor to implement a method for predicting the activity of ulcerative colitis.

[0067] The prediction method includes: based on the comparison of the concentration of sENG contained in the subject's sample with a predetermined threshold, and based on the comparison results, generating a prediction result of the subject's ulcerative colitis disease activity; The predetermined threshold is obtained by directly generating the ROC curve.

[0068] Comparative Example 1 CRP is currently the most commonly used biomarker in clinical practice to help assess the activity of ulcerative colitis (UC). This comparative example uses CRP as a biomarker to predict UC disease activity based on data from UC cohort 1 and control cohort 1 in Example 1.

[0069] Figure 4 ROC curve analysis suggests that when CRP is used to differentiate between the clinical remission phase and the active phase of UC ( Figure 4 Figure A in the figure (AUC=0.6626, p<0.001), and the mucosal inflammation resolution and active phases under endoscopy ( Figure 4 Figure B in the study (AUC=0.7161, p<0.001) showed that the efficacy was inferior to that of serum sENG.

[0070] Comparative Example 2 This comparative study used CRP as a marker to predict UC disease activity based on data from UC cohort 2 and control cohort 2 in Example 2.

[0071] Figure 6 ROC curve analysis showed that, compared with sENG, CRP had a significantly lower AUC value for identifying clinically active UC, and CRP also had a significantly lower AUC value for identifying UC in the active phase of endoscopic mucosal inflammation.

[0072] In summary, sENG is significantly more effective than CRP in differentiating between clinically active UC and endoscopically active mucosal inflammatory UC.

[0073] In summary, this invention provides a novel peripheral venous blood-based marker—sENG. sENG demonstrates excellent efficacy in predicting the clinical activity of UC patients and the activity of colitis mucosal inflammation, outperforming the currently most commonly used CRP. This indicator is inexpensive, time-saving, non-invasive, and easily repeated by patients, providing a new means for clinical monitoring of the dynamic changes in UC.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. The use of a reagent for detecting biomarkers in the preparation of products for predicting the activity of ulcerative colitis or in the preparation of diagnostic products for ulcerative colitis, characterized in that, The biomarker is a secretory endothelial glycoprotein, which includes CD105.

2. The application according to claim 1, characterized in that, The biomarkers are derived from peripheral blood, body fluids, or umbilical cord blood.

3. The application according to claim 1, characterized in that, The reagent used to detect the biomarker is a reagent used to detect the protein level of the biomarker.

4. The application according to claim 3, characterized in that, The reagents for the biomarkers are those used to detect the levels of the biomarker proteins in the samples using ELISA, Western blotting, and / or Elisopt methods.

5. The application according to claim 1, characterized in that, The product for predicting the activity of ulcerative colitis or the diagnostic product for ulcerative colitis is selected from at least one of reagents, test strips, kits, or chips.

6. The application according to claim 1, characterized in that, The kit is selected from ELISA kits, immunoturbidimetric kits, or flow cytometry kits.

7. The application according to claim 1, characterized in that, The application includes at least one of the following application methods: (1) Based on the characteristic data of the biomarkers contained in the subject's sample, generate the predicted results of the subject's ulcerative colitis disease activity; (2) Based on the characteristic data of the biomarkers contained in the subject's sample, generate the subject's ulcerative colitis diagnosis result.

8. The application according to claim 7, characterized in that, Based on the characteristic data of the biomarkers contained in the subject's test sample, a clinical activity prediction result for the subject's ulcerative colitis is generated, and / or, an endoscopic mucosal inflammation activity prediction result for the subject's ulcerative colitis is generated.

9. The application according to claim 8, characterized in that, Based on the comparison between the concentration of the biomarker contained in the subject's sample and a predetermined threshold, the subject's ulcerative colitis disease activity prediction result or ulcerative colitis disease diagnosis result is generated according to the comparison result. The predetermined threshold is either a threshold obtained from a directly generated ROC curve, or a threshold obtained from a ROC curve generated based on a machine learning model.

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