Use of ubiquitination as a marker for systemic juvenile idiopathic arthritis

Abstract

The application discloses a use of ubiquitination as a marker of systemic juvenile idiopathic arthritis, and relates to the technical field of biomedicine. The marker provided by the application is a gene combination of at least two genes in ubiquitination which participates in the occurrence of sJIA inflammation by mediating NLRs pathway; the ubiquitination includes HP, S100A9, S100A12, S100A8, IL6, CFH and UBE2D1; and the gene combination at least includes a combination of any one of HP and IL6 and UBE2D1. The application constructs a joint detection scheme of UBE2D1 and inflammatory factors, chemotactic factors or immune-related molecules, and can make up for the problems of insufficient stability or limited specificity of a single index to some extent, thereby improving the auxiliary diagnosis efficiency on sJIA. Meanwhile, the detection object involved in the application is clear, the detection mode is feasible, the detection can be carried out based on a blood sample, and the application has the advantages of relatively simple operation, strong clinical implementability, convenience for development into a detection kit, a detection chip or a matching detection reagent.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and more specifically to the use of ubiquitination as a marker of systemic juvenile idiopathic arthritis. Background Technology

[0002] Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in childhood, characterized by persistent inflammation of the synovial membrane of the joints, which can lead to joint damage and functional impairment. According to the ILAR classification system, JIA is divided into seven subtypes, among which systemic juvenile idiopathic arthritis (sJIA) is the most severe and characteristic subtype. It is an autoinflammatory syndrome primarily driven by innate immunity, and the specific cause is still unclear. Genetic factors, infections, and other factors may all contribute to the disease. There is no significant gender difference in affected children, with the peak incidence occurring between 5 and 7 years of age. It is a chronic disease characterized by a long course, severe symptoms, a high risk of fatal complications such as metastatic arthritis (MAS), and a high rate of disability due to arthritis. Treatment aims to rapidly control active symptoms and inflammatory responses, prevent joint destruction and functional limitations, treat related complications, and prevent growth retardation. Currently, common medications for sJIA include nonsteroidal anti-inflammatory drugs (NSAIDs), hormones, immunosuppressants, and biologics.

[0003] Early diagnosis and treatment, along with choosing the correct treatment approach, are crucial for sJIA. However, the diagnosis of sJIA currently relies primarily on a process of elimination, starting with common diseases and ruling out infections, malignant tumors, other autoimmune diseases, and other conditions, requiring a comprehensive analysis. Early identification is challenging, as some patients initially present only with systemic symptoms, and arthritis may appear later. In areas with high prevalence of infections such as malaria, unexplained fever can easily occur, interfering with the diagnosis of sJIA. Therefore, specific diagnostic indicators are essential for early diagnosis and accurate subtyping of the disease.

[0004] In existing technologies, the S100 protein family is elevated in various inflammatory diseases, but it cannot distinguish between sJIA and FMF, making it difficult to use for early screening. IL-18 can also be elevated in diseases such as XIAP deficiency and NLRC4 mutation, but its specificity during remission is low (only 33%). IL-6 / IL-1β lacks disease specificity, requires dynamic monitoring, and has limited value for a single test.

[0005] Ubiquitination is a crucial post-translational modification that regulates various biological processes, including immune signaling pathways, cellular stress responses, and protein homeostasis, through the ubiquitin-proteasome system. Ubiquitination precisely controls the transcriptional activity of inflammatory genes by regulating the degradation of key proteins, such as IκB in the NF-κB pathway. This mechanism plays a central role in maintaining immune homeostasis, and its abnormalities are closely related to the development and progression of autoimmune diseases. Previous studies have found that UBE2D1, as an E2 ubiquitin-binding enzyme, plays a key regulatory role by directly interacting with IκBα, promoting its ubiquitination and degradation, thereby activating the NF-κB pathway. This study shows that inhibiting ubiquitination significantly increases IκBα protein levels and reduces NF-κB activity, confirming the central role of ubiquitination in this pathway.

[0006] Therefore, this application proposes the use of ubiquitination as a marker for systemic juvenile idiopathic arthritis. Summary of the Invention

[0007] The purpose of this invention is to provide the use of ubiquitination as a biomarker for systemic juvenile idiopathic arthritis, in order to solve the problem of insufficient specificity and sensitivity in the early diagnosis of systemic juvenile idiopathic arthritis in the prior art, and to promote the clinical translation of "early screening and early diagnosis".

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] On one hand, the present invention provides a biomarker for early diagnosis of systemic juvenile idiopathic arthritis, wherein the biomarker is a gene combination of at least two genes involved in ubiquitination that mediates NLR pathways in the occurrence of sJIA inflammation; the ubiquitination includes HP, S100A9, S100A12, S100A8, IL6, CFH and UBE2D1; including at least one of HP and IL6 and UBE2D1.

[0010] Preferably, the gene combination is a combination of at least one of HP and IL6 with UBE2D1.

[0011] Preferably, the gene combination is any one of HP and IL6, and any one of S100A9, S100A12, S100A8 and CFH combined with UBE2D1.

[0012] Preferably, the gene combination is any two of S100A9, S100A12, S100A8 and CFH, combined with IL6 and UBE2D1.

[0013] Preferably, the gene combination is any two of S100A9, S100A8 and S100A12 combined with HP and UBE2D1.

[0014] Preferably, the gene combination is any one of S100A9, S100A8 and IL6 combined with CFH, HP and UBE2D1.

[0015] Preferably, the gene combination is a combination of S100A9, HP, IL6 and UBE2D1.

[0016] Preferably, the gene combination is any one of HP and IL6, any three of S100A9, S100A12, S100A8 and CFH, and a combination with UBE2D1.

[0017] Preferably, the gene combination is S100A9 or S100A8, combined with CFH, HP, IL6 and UBE2D1.

[0018] On the other hand, the present invention also provides the application of a biomarker for the early diagnosis of systemic juvenile idiopathic arthritis in the preparation of a diagnostic reagent or kit for the diagnosis of systemic juvenile idiopathic arthritis.

[0019] On the other hand, the present invention also provides the application of reagents for detecting the expression levels of the above-mentioned biomarkers in the preparation of products for diagnosing systemic juvenile idiopathic arthritis.

[0020] Preferably, the product for diagnosing systemic juvenile idiopathic arthritis is a reagent kit or a gene chip.

[0021] Preferably, the test sample for the product used to diagnose systemic juvenile idiopathic arthritis is derived from in vitro serum.

[0022] Compared with existing technologies, the beneficial effects of this solution are:

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] This invention is the first to use ubiquitination-related molecules for the auxiliary diagnosis and / or disease identification of sJIA. It has been found that ubiquitination-related biomarkers, represented by UBE2D1, have good discriminative value in sJIA, providing new molecular evidence for the early identification of this disease. Furthermore, this invention not only focuses on single biomarkers but also constructs a combined detection scheme for UBE2D1 with inflammatory factors, chemokines, or immune-related molecules. This can, to some extent, compensate for the insufficient stability or limited specificity of single indicators, thereby improving the auxiliary diagnostic efficacy for sJIA. Meanwhile, the detection targets involved in this invention are clearly defined, and the detection method is feasible. Detection can be carried out based on blood samples, offering advantages such as relatively simple operation, strong clinical feasibility, and ease of development into detection kits, detection chips, or complementary detection reagents. In addition, the biomarker combinations proposed in this invention help reveal the molecular characteristics of sJIA from the perspective of ubiquitination abnormalities and inflammatory immune imbalances, providing new ideas and basis for the precise stratification, activity assessment, and subsequent targeted intervention research of sJIA. Attached Figure Description

[0025] Figure 1 For the screening of candidate key modules, identification of hub genes, and validation of UBE2D1 expression in different cohorts (A is a heatmap of module-trait correlation, B is a correlation analysis diagram of module membership degree and gene significance in green modules, C is a weight ranking diagram of candidate hub genes in green modules, D is the expression difference of UBE2D1 in the training set, E is the expression difference of UBE2D1 in the internal validation set, and F is the expression difference of UBE2D1 in the external validation set). Detailed Implementation

[0026] To facilitate understanding of the present invention by those skilled in the art, the technical solution of the present invention will be further described in detail below with reference to embodiments and accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0027] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0028] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0030] Example 1: Screening markers

[0031] This embodiment retrieves transcriptome expression data related to systemic juvenile idiopathic arthritis (sJIA) from the GEO database and filters the dataset to include both sJIA samples and healthy control samples. The obtained expression profile data is preprocessed, including probe annotation, gene symbol conversion, duplicate probe merging, and normalization. Subsequently, candidate gene expression matrices are extracted based on a pre-organized set of ubiquitination-related genes.

[0032] To screen for biomarkers with high predictive performance, the following methods were used:

[0033] Gene expression profiles related to sJIA were retrieved from the GEO database, and datasets containing sJIA patient samples and healthy control samples were selected as the research subjects. The obtained data underwent preprocessing, including data download, probe annotation, gene symbol conversion, removal of duplicate probes, handling of missing values, and standardization. For cases where multiple probes correspond to the same gene, the expression value of the gene could be determined using the average method, median method, or by selecting the probe with the highest expression level.

[0034] After data preprocessing, a gene co-expression network was first constructed using weighted gene co-expression network analysis (WGCNA) on the post-GSE11205738 individuals. By selecting appropriate soft threshold parameters, an approximately scale-free network was established, and hierarchical clustering of genes was performed based on the topological overlap matrix to further identify different co-expression modules. Subsequently, correlation analysis was performed between each module and the disease phenotype to screen key modules significantly associated with sJIA. Furthermore, candidate genes closely related to the disease phenotype within each module were screened based on module membership (MM) and gene significance (GS).

[0035] After obtaining candidate genes, a consensus machine learning model is further used for feature screening and diagnostic efficacy evaluation. Specifically, candidate genes are incorporated into multiple machine learning models for training, and core genes that consistently appear are selected based on the importance of genes, feature contribution, or regression coefficients in different models. The training set consisted of GSE112057 (38 participants), the internal validation sets consisted of GSE7753 (47 participants) and GSE13501 (70 participants), and the external validation set consisted of the Chongqing Medical University Children's Hospital cohort. Since systemic juvenile idiopathic arthritis (sJIA) is a rare disease with a small sample size, and a query of the GEO database for sJIA transcriptome sequencing revealed that the GSE7753 dataset was of higher quality compared to other datasets, this embodiment used self-test data (18 participants) as the external validation set to improve the accuracy of early diagnosis of sJIA. The self-test data came from sJIA patients who visited the Department of Rheumatology and Immunology at Chongqing Medical University Children's Hospital from January 2023 to January 2025, including 10 males and 8 females with a mean age of 9.56 years; the control group consisted of 9 healthy individuals. The preferred machine learning models included two or more of the following: LASSO, support vector machine recursive feature elimination, random forest, generalized linear model, extreme gradient boosting tree, and linear discriminant analysis. Genes with the highest weights that are repeatedly selected in multiple models will be identified as candidate diagnostic biomarkers to improve the stability and reliability of the screening results.

[0036] Furthermore, diagnostic models were constructed for the selected candidate biomarkers, and their discriminative ability for sJIA was evaluated using receiver operating characteristic (ROC) curves. The area under the curve (AUC), sensitivity, and specificity were calculated. Genes with high AUC values ​​and stable performance in different models were identified as the core biomarkers of this invention. Based on the above analysis, this embodiment screened biomarkers with auxiliary diagnostic value for sJIA, as follows:

[0037] Table 1. AUC values ​​of candidate key genes

[0038]

[0039] As shown in Table 1, the top 7 genes with the highest AUC in sJIA patients are HP, S100A9, S100A12, S100A8, IL6, CFH, and UBE2D1, all with AUC values ​​greater than 0.7. Except for CFH, the specificity of the other genes is no less than 0.87, but the sensitivity of CFH is greater than 0.9. Therefore, these seven ubiquitinated genes can serve as biomarkers for the early diagnosis of systemic juvenile idiopathic arthritis, but their limitations remain.

[0040] Example 2

[0041] Previous studies in this embodiment have revealed that UBE2D1 plays a key regulatory role as an E2 ubiquitin-binding enzyme. UBE2D1 directly interacts with IκBα, promoting its ubiquitination and degradation, thereby activating the NF-κB pathway. Therefore, this embodiment verified the expression of UBE2D1 in different cohorts through screening of candidate key modules and identification of pivot genes. The results are shown in […]. Figure 1 .

[0042] A heatmap illustrating the correlation between modules and traits was used to demonstrate the relationships between different co-expressed modules and the studied traits. The results showed that the green modules exhibited a significant correlation with the target traits and were therefore selected as the focus of further analysis. The significance of this heatmap lies in completing the "module screening" step at the overall network level, providing a basis for subsequent pivot gene discovery (see...). Figure 1 (A in the middle).

[0043] The correlation between module membership and gene significance in the green module was analyzed. The results showed a significant positive correlation between module membership and gene significance within the green module (cor=0.5, p=1.6×10^-167), suggesting that genes closer to the module core tend to have a stronger association with the target trait. This result indicates that the green module is not only associated with traits, but its core genes also have high biological significance, supporting further screening of key candidate genes within this module (see...). Figure 1 (B in the middle).

[0044] The weighted ranking of candidate hub genes in the green module showed that UBE2D1 ranked highly, indicating its significant importance in feature selection or model construction. This suggests that UBE2D1 was not randomly selected, but rather stood out in the comparison of candidate genes, thus providing a basis for its selection as a key research subject (see...). Figure 1 (C in the middle).

[0045] Therefore, the expression differences of UBE2D1 in the training set, internal validation set, and external validation set were further investigated. The results showed that, in the training set, compared with the control group (healthy individuals), the expression of UBE2D1 in the sJIA training set was significantly increased, and the difference was statistically significant. This result indicates that UBE2D1 demonstrated good discriminative ability in the initial discovery cohort, which forms the basis for subsequent validation analysis (see...). Figure 1 (D in the text). In the internal validation set, UBE2D1 expression in the sJIA group was still higher than that in the control group, and the overall trend was consistent with that in the training set, suggesting that the difference in UBE2D1 expression has good internal repeatability and stability. This proves that the results are not a coincidence of the training set (see D). Figure 1(E in the original text). In the external validation set, UBE2D1 expression in the sJIA group was also significantly higher than that in the control group, indicating that UBE2D1 maintained a consistent expression trend across independent data sources. This result further supports the good cross-cohort stability and potential biomarker value of UBE2D1 (see [link to original text]). Figure 1 (F in the middle).

[0046] To improve the accuracy of early diagnosis of systemic juvenile idiopathic arthritis, based on the data from Example 1, this example randomly sampled from the Top 6 and paired them with UBE2D1 to calculate the ROC curve, resulting in a total of 64 combinations. The results are shown in Table 2.

[0047] Table 2. AUC values ​​of the genome combined with UBE2D1

[0048] The results showed that the ROC curves of gene combinations significantly increased after combining with UBE2D1. Specifically, combinations of any one or more of HP and IL6 with UBE2D1; or combinations of any one of S100A9, S100A12, S100A8, and CFH with UBE2D1 and HP or UBE2D1 and IL6; or combinations of any two of S100A9, S100A12, S100A8, and CFH with IL6 and UBE2D1; or combinations of any two of S100A9, S100A8, and S100A12 with HP and UBE2D1; or combinations of S100A9, S1... The following combinations of S100A8 and IL6 with CFH, HP, and UBE2D1; or S100A9, HP, IL6, and UBE2D1; or HP and IL6 combined with any three of S100A9, S100A12, S100A8, and CFH, and then combined with UBE2D1; or S100A9 or S100A8 combined with CFH, HP, IL6, and UBE2D1; with an AUC value not less than 0.93, a sensitivity not less than 0.86, and a specificity not less than 0.90, demonstrate that the above-mentioned genomic markers can be used as biomarkers for the early diagnosis of systemic juvenile idiopathic arthritis, exhibiting high specificity and sensitivity.

[0049] This invention constructs a combined detection scheme for UBE2D1 with inflammatory factors, chemokines, or immune-related molecules, which can, to some extent, compensate for the insufficient stability or limited specificity of single indicators, thereby improving the auxiliary diagnostic efficacy for sJIA. Meanwhile, the detection targets involved in this invention are clearly defined, the detection method is feasible, and detection can be carried out based on blood samples. It has the advantages of relatively simple operation, strong clinical feasibility, and ease of development into detection kits, detection chips, or supporting detection reagents. Furthermore, the biomarker combination proposed in this invention helps to reveal the molecular characteristics of sJIA from the perspective of ubiquitination abnormalities and inflammatory immune imbalances, providing new ideas and basis for the precise stratification, activity assessment, and subsequent targeted intervention research of sJIA.

[0050] The above specific embodiments are merely explanations of the present invention and are not intended to limit the present invention. After reading this specification, those skilled in the art can make modifications to these embodiments without contributing any inventive step, but as long as they are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis, characterized in that, The biomarker is a combination of at least two genes involved in ubiquitination that mediates NLR pathways in the development of sJIA inflammation; the ubiquitination includes HP, S100A9, S100A12, S100A8, IL6, CFH and UBE2D1; the gene combination includes at least one of HP and IL6 combined with UBE2D1.

2. The biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is a combination of at least one of HP and IL6 with UBE2D1.

3. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is any one of HP and IL6, and any one of S100A9, S100A12, S100A8 and CFH combined with UBE2D1.

4. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is any two of S100A9, S100A12, S100A8 and CFH, combined with IL6 and UBE2D1.

5. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is any two of S100A9, S100A8, and S100A12 combined with HP and UBE2D1.

6. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is any one of S100A9, S100A8 and IL6 combined with CFH, HP and UBE2D1.

7. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is a combination of S100A9, HP, IL6 and UBE2D1.

8. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is any one of HP and IL6, any three of S100A9, S100A12, S100A8 and CFH, and a combination with UBE2D1.

9. A biomarker for early diagnosis of systemic juvenile idiopathic arthritis according to claim 1, characterized in that, The gene combination is S100A9 or S100A8, combined with CFH, HP, IL6 and UBE2D1.

10. The use of a biomarker for early diagnosis of systemic juvenile idiopathic arthritis as described in any one of claims 1 to 9 in the preparation of a diagnostic reagent or kit for diagnosing systemic juvenile idiopathic arthritis.

11. The use of a reagent for detecting the expression level of a biomarker for early diagnosis of systemic juvenile idiopathic arthritis as described in any one of claims 1 to 9 in the preparation of products for diagnosing systemic juvenile idiopathic arthritis.

12. The application according to claim 11, characterized in that, The products used to diagnose systemic juvenile idiopathic arthritis are kits or gene chips.

13. The application according to claim 11, characterized in that, The test samples for the product used to diagnose systemic juvenile idiopathic arthritis are derived from ex vivo serum.

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