TWI755750B - A method for diagnosis and subtyping of adult-onset still's disease - Google Patents

Abstract

The invention relates to a method for diagnosis of a subject suffering from adult-onset Still's disease and further to determine the disease course of the subject suffering from adult-onset Still's disease.

Description

Methods for the diagnosis and classification of adult-onset Still's disease

The present invention relates to a method of diagnosing and classifying subjects with adult-onset Still's disease.

Adult-Onset Still's Disease (AOSD) is an idiopathic inflammatory disorder characterized by fever, rash, arthralgia or arthritis, multisystem involvement, and elevated levels of acute-phase reactants. Diagnosis is often delayed due to persistent spiking fever. Most patients are diagnosed only after developing secondary symptoms and ruling out more common conditions such as infection or other malignant disease. Despite these difficulties, the Yamaguchi criteria provide the highest sensitivity and accuracy for AOSD diagnosis among current clinical diagnoses.

It is also characterized by cytokines associated with T helper type 17 (Th17) and pro-inflammatory cytokines, such as tumor necrosis factor (TNF)-alpha, interleukin (IL)-1beta, IL-6, IL-8, and IL-18 ; however, the exact pathogenesis remains elusive. Recent reports point to the association of viral infection with AOSD. Previous studies have also shown that the expression of the NLRP3 inflammasome is elevated in AOSD patients. In addition, an increase in circulating microRNA-134 was observed in AOSD patients, possibly regulated by toll-like receptor (TLR)-3 ligand (poly I:C), and its expression was positively correlated with disease activity. Despite this evidence, diagnostic markers are still lacking, and the diagnosis of AOSD is usually based on clinical criteria.

The course of AOSD can vary widely and can be divided into two main subtypes, After the difference: systemic subtype (systemic subtype) and articular subtype (articular subtype). Although proinflammatory cytokines contribute to AOSD pathogenesis, their use in predicting disease progression is limited due to the extensive overlap between subtypes. Several investigators, including us, have revealed significantly elevated levels of TNF-α in patients with the articular subtype of AOSD, and biologics targeting this patient have demonstrated that TNF-α is effective in the treatment of AOSD patients.

From mild to severe manifestations, the course of AOSD is difficult to assess. Therefore, accurate assessment of disease course is important for treatment decisions. Several acute phase reactants, including C-reactive protein and ferritin, are used in the laboratory to monitor AOSD disease activity. However, these markers are nonspecific and do not correlate well with disease progression. The Pouchot score can be used to assess disease progression, including elevated liver enzymes, pericarditis, myalgia, neutropenia, and lymphadenopathy.

For the treatment of AOSD, the first-line treatments for AOSD are non-steroidal anti-inflammatory drugs (NSAIDs) and glucocorticoids. After a lack of clinical response to first-line therapy, second-line therapy, including synthetic disease-modifying anti-rheumatic drugs (DMARDS) and biologics. The use of DMARDS such as methotrexate, azathioprine and leflunomide is commonly used to reduce the amount of corticosteroids used. Biologics, including intravenous immunoglobulins, anti-TNF- α drugs such as etanercept, infliximab, and adalimumab, and anti-IL-6 drugs such as tocilizumab, also appear to adequately control disease in those who do not respond to conventional therapy.

Long non-coding RNAs (lncRNAs), non-protein-coding transcripts longer than 200 nucleotides, have emerged in different stages of gene regulation. effect. LncRNAs can participate in chromatin remodeling, transcriptional processes and cellular responses through RNA-DNA, RNA-RNA or RNA-protein interactions. Recent studies have also shown that lncRNAs can be induced to express and act as key regulators of inflammatory responses in immune cells. Additionally, lncRNAs have been implicated in innate immunity against viral infection. Although more and more lncRNAs have been discovered, their biological functions and mechanisms of action are largely unknown.

The present invention is of particular interest to six lncRNAs that are involved in immune regulation or inflammatory responses and may be involved in the pathogenesis of AOSD or viral infection. The six lncRNAs are MIAT (10.18kb, NONHSAT192181.1), THRIL (1.98kb, NONHSAT164169.1), NTT (17.57kb, NONHSAT115106.2), RMR (0.27kb, NONHSAT130962.2), PACERR (0.83kb) , NONHSAT150184.1 and NEAT1 (22.74kb, NONHSAT022112.2). MIAT (myocardial infarction associated transcript) is known to be involved in the pathogenesis of various diseases, including: myocardial infarction, microvascular dysfunction and myalgic encephalomyelitis/chronic fatigue Syndrome (myalgic encephalomyelitis/chronic fatigue syndrome, ME/CFS). THRIL (TNFα and hnRNPL-related immunoregulatory lincRNA) acts as a scaffold by interacting with heterogeneous nuclear ribonucleoprotein L (hnRNPL) and this complex interacts with TNF-α promoter binds to induce its transcription upon activation of TLR2. NTT (non-coding transcript in T cells) is in activated CD4 ⁺ T cells stimulated by human immunodeficiency virus (HIV) peptides found, showing its role in adaptive immunity. Previous studies have shown that NTT is a regulator of monocyte inflammation, and its activation is involved in monocyte/macrophage differentiation and contributes to rheumatoid arthritis ( Pathogenesis of rheumatoid arthritis, RA). RMRP (RNA component of the mitochondrial RNA-processing endoribonuclease) promotes RORγt DDX5 assembly and is recruited to key genomes specifically involved in the RORγt-occupied Th17 effector program. PACERR (p50-associated COX- 2 extragenic RNA, also known as PACER) is a positive regulator of COX-2 expression in human epithelial cells and macrophage-like cells after TLR4 activation. NEAT1 (nuclear enriched abundant transcript 1) is essential for the formation of nuclear body paraspeckles) are crucial and contribute to IL -8 Transcriptional activation. NEAT1 expression was also increased upon stimulation with the TLR3 ligand poly(I:C), a synthetic analog representing double-stranded RNA for active viral infection. However, there are no research data on circulating lncRNA expression in AOSD patients.

In the present invention, the expression profile and potential diagnostic value of six lncRNAs in AOSD patients were investigated. Patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) were also included in this study. The effect of TLR3 ligand poly(I:C) and TLR4 ligand LPS on lncRNA expression was further evaluated in the human monocyte cell line THP-1. Additionally, prediction scores derived from the expression of lncRNAs and proinflammatory cytokines were established for AOSD diagnosis and disease progression prediction.

The present invention relates to a method for detecting that an individual suffers from adult-onset Strudel's disease, the method comprising the following steps: (a) providing a blood sample and a peripheral blood mononuclear cell; detecting a protein biomarker in the blood sample Expression level, wherein the protein biomarker is IL-18; (b) detecting the expression level of a long-chain non-coding RNA biomarker in the peripheral blood mononuclear cells, wherein the long-chain non-coding RNA biomarker The marker is MIAT or THRIL; (c) the expression levels of IL18, MIAT and THRIL detected in step (b) are used to calculate an A-score with a predictive function, wherein the predictive function is determined by a multiple regression analysis ( multiple regression analysis) and (d) comparing the A-score calculated in step (c) with a threshold, wherein the threshold is obtained by a Receiver Operator Characteristic (ROC) method, wherein the threshold corresponds to The area under an ROC curve (AUC) reaches the maximum value, where the A-score is higher than the critical value, indicating that the individual suffers from adult-onset Stubbs disease.

In one aspect, the invention relates to a method of detecting the disease course of an individual suffering from adult-onset Stubbs' disease, the method comprising the steps of: (a) providing a blood sample and a peripheral blood mononuclear cell, wherein the The blood sample and the peripheral blood mononuclear cell line are collected from an individual suffering from adult-onset Stubbs' disease; (b) detecting the expression of a protein biomarker in the blood sample, wherein the protein biomarker The marker is TNF-α; the expression level of a long-chain non-coding ribonucleic acid biomarker in the peripheral blood mononuclear cells is detected, wherein the long-chain non-coding ribonucleic acid biomarker is NTT; (c) adding step ( b) the resulting detected expression of TNFα and NTT calculates a B-score with a predictive function, wherein the predictive function is obtained by a multiple regression analysis; and (d) applying step (c) The calculated B-score is compared to a threshold obtained by a receiver operator characteristic (ROC) method, wherein the threshold corresponds to the area under an ROC curve curve, AUC) reaches the maximum value, wherein the B-score higher than the cut-off value indicates that the disease course of the individual suffering from adult-onset Stubbs is a systemic subtype or an articular subtype.

In another aspect, the present invention relates to a kit for diagnosing an individual with adult-onset Stubbs' disease, wherein the kit comprises a reagent selected from the group consisting of: (a) for detecting IL-18 protein Reagents for ; (b) Reagents for lncRNA Detection of MIAT and THRIL; and (c) Instruction manual providing prediction equations and cutoff values for determining whether a subject has adult-onset Stubbs disease.

In another aspect, the present invention relates to a kit for diagnosing the course of an individual with adult-onset Stubbs disease, wherein the kit comprises reagents selected from the group consisting of: (a) reagents for the detection of TNF- alpha protein; (b) reagents for the detection of lncRNAs for NTT; and (c) an instruction manual providing prediction equations and cutoff values for determining whether individuals with adult-onset Stubbs are the systemic subtype or the Arthritis subtype.

Figure 1. Expression profiles of lncRNAs and cytokines in AOSD patients and healthy controls. af. From healthy controls (Control), patients with systemic subtype of AOSD (AOSD-systemic) and patients with articular subtype of AOSD (AOSD-arthritis), a: MIAT, b: THRIL, c: NTT, d: NEAT1, e : RMRP, f: PACERR. g-k. Plasma cytokine expression was detected in healthy controls, patients with the systemic subtype of AOSD, and patients with the articular subtype of AOSD. g: IL-1α, h: IL-6, iIL-17A, j: IL-18, k: TNF-α. Lines represent medians. *: p<0.05, **: p<0.01, ***: p<0.001 calculated by Kruskal-Wallis tests.*: p<0.05, **: p<0.01, ***: p<0.001, by kruskal -Wallis test calculation.

Figure 2. AOSD patients are differentiated from healthy controls by expression profiles of MIAT, THRIL and IL-18. a. 3D scatter plot showing the expression of MIAT, THRIL and IL-18 for each sample. Circles: healthy controls; triangles: AOSD patients (including both subtypes). b. The receiver operating characteristic curve (ROC curve) of the AOSD prediction score (A score) can be obtained from the expression levels of MIAT, THRIL and IL-18. In predicting the diagnosis of AOSD, the area under the curve (AUC) = 0.998, the sensitivity = 94.87%, the specificity when the cut-off value > 7.114 Sex = 100%.

Figure 3. Disease outcomes for AOSD subtypes differentiated by NTT and TNF-α expression characteristics. a. 3D scatter plot showing the expression of NTT, TNF-α and IL-18 for each sample. Circles: AOSD-articular subtype; triangles: AOSD systemic subtype. b. ROC curve analysis of AOSD subtype prediction scores (B scores) derived from NTT and TNF -α expression levels. In predicting the systemic subtype of AOSD in AOSD patients, AUC=0.855 (cutoff >0.266, sensitivity=66.7%, specificity=90.9%).

Figure 4. Correlation of lncRNA expression with cytokine or AOSD disease activity. a. Association of low RMRP and PACERR expression with AOSD disease activity. All AOSD patients were divided into (RMRP dCT+PACERR dCT)>17 or (RMRP dCT+PACERR dCT)<17, and the disease activity levels of the two groups were compared by Mann-Whitney U test. b. Correlation of IL-18 expression with AOSD disease activity (AOSD, n=38). By linear regression analysis, r ² =0.38, p<0.0001. c. Paired plots show scatter plots (plotted by R software) between all lncRNAs and cytokine expression in each AOSD patient sample. *: p<0.05. Bars represent mean ± SEM.

Figure 5. Comparison of lncRNA expression patterns before and after treatment in AOSD patients. a-b. Changes in NEAT1 expression in AOSD patients treated with prednisolone or cyclosporin (a) or the IL-6 receptor inhibitor: tocilizumab (b) alone. Tx: Treatment. c. Correlation of NEAT1 and PIK3CA dCT expression levels before and after treatment (all treatments). d. Paired plots showing the correlation between the expression of each two lncRNAs before and after treatment and the correlation between the lncRNA and the amount of PIK3CA dCT expression. The Pearson correlation coefficient and its p-value are shown in the lower right corner of the graph.

Figure 6. Network analysis of interactions of lncRNA-associated molecules using STRING (protein-protein interaction network functional enrichment analysis). Dark grey origins represent molecules of inflammatory response-rich pathways (GO: 0006954).

Figure 7. LncRNA expression patterns in the human monocytic cell line THP-1 upon stimulation with poly(I:C) or LPS. THP-1 cells were stimulated with 50 μg/ml poly(I:C) or 100 ng/ml LPS for 4 hours or 24 hours. Only THP-1 cells cultured in LPS-free RPMI medium for the indicated times were used as controls for each experiment. The amount of expression of lncRNA or PIK3CA was measured by real-time quantitative PCR. a. Expression of six lncRNAs. b. The amount of expression of PIK3CA. 4h poly(I:C) stimulation: n=6-8; 4h LPS stimulation: n=3-5: 24h poly(I:C) stimulation: n=5-6; 24h LPS stimulation: n=3-5. Bars represent mean ± SEM.

Figure 8. Expression of MIAT and IL-18 in RA and SLE transcriptomes. a-b. Meta-transcriptomics analysis of expression levels of MIAT (a) and IL-18 (b) in RA. c-d. Meta-transcriptomics analysis of expression levels of MIAT (c) and IL-18 (d) in SLE. Global transcriptomic analysis was performed using the MetaSignature website (http://metasignature.stanford.edu/). Each box represents a disease cohort transcriptome dataset selected from the Gene Expression Omnibus (GEO) repository via the MetaSignature website.

Figure 9. Differentiation of AOSD samples from RA and SLE samples using combined MIAT (dCT), THRIL (dCT) and IL-18 (pg/ml) expression patterns. a. The amount of MIAT (dCT), THRIL (dCT) and IL-18 (pg/ml) expressed for each sample is shown in a 3D scatterplot. Diamond: AOSD; cross: RA; triangle: SLE. Diamond: AOSD; cross: RA; triangle: SLE. b. MIAT (dCT), THRIL (dCT) and IL-18 (pg/ml) of PBMCs of AOSD, RA and SLE patients Principal Component Analysis (PCA) of the values. c-d. MIAT dCT values in RA and SLE PBMCs compared to healthy controls. e. Relative expression of MIAT in sepsis and healthy control blood cells (GSE 28750 data). n.s. not significant, *p<0.05 (by Mann-Whitney U test).

Figure 10. Comparison of lncRNA expression patterns in AOSD, RA, SLE and sepsis patients. ab. Receiver operating characteristic curve of AOSD prediction score (A-score) for identifying RA from AOSD (a: AUC=0.829 at cutoff >12.57) or SLE from AOSD (b: AUC=0.881 at cutoff >11.39) (receiver operating characteristic curve, ROC curve). c. PIK3CA expression in AOSD, RA, SLE or sepsis compared to PBMC in healthy controls. Lines represent medians. *p<0.05, ***p<0.001 by Mann-Whitney U test. PIK3CA dCT was calculated by subtracting GAPDH CT values. The sepsis microarray blood cell transcriptome dataset GSE28750, collected from the Gene Expression Omnibus (GEO), shows the relative expression of PIK3CA in blood cells in sepsis and healthy controls. n.s: Not statistically significant according to the Mann-Whitney U test. d. Correlation between MIAT and PIK3CA expression in blood cells of patients with AOSD, RA, SLE or sepsis. The Pearson correlation coefficient r/p value is displayed in the lower right corner of the graph.

The present invention relates to a method for detecting that an individual suffers from adult-onset Strudel's disease, the method comprising the following steps: (a) providing a blood sample and a peripheral blood mononuclear cell; detecting a protein biomarker in the blood sample Expression level, wherein the protein biomarker is IL18; (b) detecting the expression level of a long-chain non-coding RNA biomarker in the peripheral blood mononuclear cells, wherein the long-chain non-coding RNA biomarker is MIAT or THRIL; (c) calculates an A-score with a predictive function of the detected expression levels of IL-18, MIAT and THRIL obtained in step (b), wherein the predictive function is determined by a multiple regression analysis ( multiple regression analysis) and (d) comparing the A-score calculated in step (c) with a threshold, wherein the threshold is obtained by a Receiver Operator Characteristic (ROC) method, wherein the threshold corresponds to The area under an ROC curve (AUC) reaches the maximum value, where the A-score is higher than the critical value, indicating that the individual suffers from adult-onset Stubbs disease.

In the above method, the method further comprises the step of: (e) administering to the subject an effective amount of a therapeutic drug for AOSD.

In one embodiment, the prediction equation is: A-score = 1.62 * THRIL ΔCT - 1.43 * MIAT ΔCT + 0.02 * IL-18 (pg/ml)

In another embodiment, the cut-off value is between 5.85 and 7.12, the sensitivity is 94.4%-100%, and the specificity is 93.7-100%.

In another embodiment, the cutoff value is 7.114, the sensitivity is 94.87%, and the specificity is 100%.

In the above method, the gene expression level of a PIK3CA in the peripheral blood mononuclear cells is further detected, and when the PIK3CA gene expression level is higher than that of a healthy individual, the individual is excluded from systemic lupus erythematosus, rheumatoid arthritis or sepsis.

In the above method, wherein the healthy individual is an individual not suffering from a rheumatic disease.

In the above method, the method for detecting the expression level of the protein biomarker in step (b) is an immunological assay.

In the above method, the method for detecting the expression level of lncRNA in step (b) is polymerase chain reaction.

In the above method, wherein the blood sample is whole blood, serum or plasma.

In another aspect, the invention relates to a method of detecting the disease course of an individual suffering from adult-onset Stubbs disease, the method comprising the steps of: (a) providing a blood sample and a peripheral blood mononuclear cell, wherein The blood sample and the peripheral blood mononuclear cells are collected from an individual with adult-onset Stubbs' disease; (b) detecting the expression of a protein biomarker in the blood sample, wherein the protein biomarker The marker is TNF-α; the expression of a long-chain non-coding ribonucleic acid biomarker in the peripheral blood mononuclear cells is detected, wherein the long-chain non-coding ribonucleic acid biomarker is NTT; (c) adding step (b) the resulting detected expression of TNFα and NTT calculates a B-score with a predictive function, wherein the predictive function is obtained by a multiple regression analysis; and (d) applying step (c) ) The calculated B-score is compared to a threshold obtained by the Receiver Operator Characteristic (ROC) method, wherein the threshold corresponds to the area under the ROC curve (the area under an ROC curve). ROC curve, AUC) reaches the maximum value, wherein the B-score is higher than the critical value, indicating that the individual disease course of adult-onset Stubbs disease is a systemic subtype or a joint subtype.

In the above method, the method further comprises the step of: (e) administering to the subject an effective amount of a therapeutic drug for AOSD.

In one embodiment, the prediction equation is: B-score = 0.22 * NTT ΔCT - 0.01 * TNF-α(pg/ml) .

In another embodiment, the cut-off value is between 0.17 and 0.83, the sensitivity is 57.1%-71.4%, and the specificity is 66.7-100%.

In another embodiment, the cutoff value of 0.266, with sensitivity of 66.7% and specificity of 90.9%. was 66.7% and the specificity was 90.9%

In the above method, the method for detecting the expression level of the protein biomarker in step (b) is an immunological assay.

In the above method, the method for detecting the expression level of lncRNA in step (b) is polymerase chain reaction.

In the above method, wherein the blood sample is whole blood, serum or plasma.

In another aspect, the present invention relates to a kit for diagnosing an individual with adult-onset Stubbs' disease, wherein the kit comprises a reagent selected from the group consisting of: (a) for detecting IL-18 protein (b) reagents for the detection of lncRNAs of MIAT and THRIL; and (c) an instruction manual providing prediction equations and cut-off values for determining whether a subject has adult-onset Stubbs disease.

In another aspect, the present invention relates to a kit for diagnosing the course of an individual with adult-onset Stubbs disease, wherein the kit comprises reagents selected from the group consisting of: (a) reagents for the detection of TNF- alpha protein; (b) reagents for the detection of lncRNAs for NTT; and (c) an instruction manual providing prediction equations and cutoff values for determining whether individuals with a course of adult-onset Stubbs disease are systemic subtypes type or articular subtype.

Example

The practical application scope of the present invention can be further proved by the following specific examples. It is only a preferred embodiment of the present invention, and does not limit the scope of the present invention. Therefore, any simple changes and modifications made in accordance with the scope of the present invention and the contents of the description of the present invention are still covered by the scope of the present invention.

method

Subjects: 41 consecutive AOSD patients who met Yamaguchi criteria. Patients with infections, malignancies, or other rheumatic diseases were excluded (Table 1).

Table 1. Diagnostic criteria for Still's disease in adults

Whole body activity was assessed in each patient with AOSD using the modified Pouchot score, where active AOSD was defined as an activity score of 4 or higher. This systemic activity score (range 0-12) assigns a score to each of the following 12 manifestations: fever, evanescent rash, sore throat, arthralgia or arthritis, myalgia, pleurisy, pericarditis, pneumonia, lymph nodes Disease, hepatomegaly or abnormal liver function, increased white blood cell count ≥ 15,000/mm3, serum ferritin level> 3000μg/L.

At the start of the study, all patients in active status were receiving NSAIDs with or without corticosteroids, but none were receiving conventional synthetic disease-modifying antirheumatic drugs (conventional synthetic disease-modifying anti-rheumatic drugs) rheumatic drugs, csDMARDs) or biological therapy. After investigation, 35 (85.4%) received at least one of the csDMARDs, including methotrexate (n=32), hydroxychloroquine (n=29), cyclosporine (n=15), and azathioprine (n=10). All AOSD patients with at least one year of follow-up were Divided into two disease subtypes: a systemic subtype that includes a single-cycle and multi-cycle form of the disease course, and a joint subtype (persistent arthritis, involving at least one joint, longer duration, more than 6 months ). Twenty patients who met the 2010 RA classification criteria of the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) collaborative initiative and 20 patients who met the 1997 revised SLE ACR criteria Patients were included as disease controls. Thirty-two age-matched healthy controls without any rheumatism were used. The institutional review board approved this study (CMUH108-REC1-099), and written consent was obtained from each participant in accordance with the Declaration of Helsinki.

RNA extraction and quantitative real-time PCR (qRT-PCR) for lncRNA: Approximately 8 ml of venous whole blood was drawn from participants' peripheral venous blood in the morning. Peripheral blood mononuclear cells (PBMC) were immediately isolated using Ficoll-Paque ^™ PLUS (GE Healthcare Biosciences, Illinois, USA) density gradient centrifugation. Place PBMCs in a -80 °C freezer until simultaneous detection of lncRNAs. Total RNA from PBMCs was extracted by TRIzol® reagent (Sigma-Aldrich, Missouri, USA) and purified using the RNeasy MinElute Cleanup Kit (QIAGEN, Germany) according to the manufacturer's instructions.

Purified RNA was quantified at OD260 and 280 nm using an ND-1000 spectrophotometer (Nanodrop Tcchnology, USA). Two patients with low nucleic acid quality were excluded from further analysis. 2 μg of RNA was reverse transcribed into cDNA for qRT-PCR analysis using a high capacity cDNA reverse transcriptase kit (ThermoFisher Scientific-Invitrogen, Waltham, Massachusetts, USA). Human glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene expression was used as an endogenous control. All quotes Both were designed and synthesized by Genomics BioSci & Tech in Taipei, Taiwan. The primer sequences used in the present invention are listed in Table 2.

Table 2. Nucleotide sequences of oligonucleotides used for qRT-PCR

qRT-PCR reactions were performed on a Roche LightCycler Instrument 480 using the IQ2 TaqMan Probe qPCR system (hermo Fisher Scientific, Massachusetts, USA). Use 40-200ng of cDNA for one cycle of real-time PCR of 30 sec preincubation at 95°C, 50 cycles of amplification (10 sec at 95°C, 30 sec at 60°C, 10 sec at 72°C), and finally at 40°C Cool for 30 seconds. The difference in expression of the gene of interest relative to the average endogenous control gene in each sample was calculated using the comparative threshold cycle (CT) method.

In vitro cell line studies: Human monocyte cell line (THP-1) (ATCC TIB-202; American Type Culture Collection, Rockville, Md.) was cultured in RPMI medium (Gibco, ThermoFisher) without LPS and supplemented with 10% fetal bovine serum. Scientific, USA) in a 37°C, 5% CO2 incubator. One million cells were cultured in RPMI medium without LPS and treated with either the Toll-like receptor (TLR) 3 ligand poly(I:C) or the TLR4 ligand LPS for 4 hours and 24 hours. THP-1 cells cultured in RPMI medium without LPS for 4 hours and 24 hours most corresponded to the experimental control group. RNA was then extracted from THP-1 cells for further qRT-PCR analysis. Differences in expression of target genes relative to the mean internal control gene in each sample were calculated using the comparative threshold cycle (CT) method and assessed by ΔCT ( ^CTlncRNA - ^CTGAPDH ). The median ΔCT value of multiple replicates of THP-1 cells in control wells was calculated. The expression fold of lncRNA in THP-1 cells was calculated by 2 ^- ΔΔCT, where the formula for ΔΔCT was: [(CT lncRNA-CT GAPDH)-average ΔCT of control gene].

Determination of pro-inflammatory cytokine expression: Cytokine expression in plasma was detected by commercial ELISA kits for IL-1β (RayBiotech Inc., Norcross, GA, USA), IL-6 (PeproTech Inc., Rocky Hill, NJ) , USA), IL-17A (RayBiotech Inc., Norcross, GA, USA), IL-18 (Medical & Biology Laboratories Co, Ltd., Naka-Ku, Nagoya, Japan), and TNF-α (R&D Systems, Minneapolis, MN, USA) were assayed according to each manufacturer's instructions . All assays were performed with inter- and intra-assay coefficients of variation (CV) of less than 10%.

Bioinformatics and statistical analysis: Kruskal-Wallis test was performed by GraphPad Prism 5 to analyze differences in lncRNA and cytokine expression between subtypes of AOSD patients and healthy controls. The generalized linear model (glm) function of R software v.3.6.0 (R Foundation for Statistical Computing, Vienna, Austria) was used to diagnose AOSD with lncRNA expression (ΔCT) and plasma cytokine expression (pg/ml) A polynomial regression analysis was performed on the variables. The lncRNA expression profile of each sample from AOSD patients and healthy controls was plotted on a 3D scatter plot by using R software and further visualized key variables to distinguish AOSD patients from controls.

The AOSD cohort was randomly divided into a test dataset (70% of the sample) and a validation dataset (30% of the sample), and a confusion matrix was constructed using the selected variables to summarize the performance of the classification algorithm (R software, glm function). The AOSD prediction score (as obtained by the above analysis) was established by the combination of variables that gave the best correct classification rate. Receiver operating characteristic curve (ROC) analysis was performed using MedCalc v.14 to determine the area under the ROC curve (AUC), sensitivity, specificity and accuracy.

Pearson's correlations were used to determine the relationship between the expression of lncRNA and the expression of pro-inflammatory cytokines or PIK3CA. Other rheumatic diseases (including RA and SLE) on the MetaSignature website (http://metasignature.stanford.edu/) Meta-analyses were performed on the transcriptomes of available lncRNA MIAT and IL-18 expression.

result

Clinical features of AOSD patients

39℃)，皮疹，關節痛或關節炎，喉嚨痛，肝功能障礙和淋巴結病。如表3所示，AOSD患者與健康對照(HC)之間的進入年齡或女性比例均無顯著差異。27例患者患有系統性亞型，其他患者患有慢性關節亞型。 Of the 41 patients initially enrolled, 2 had low nucleic acid quality, while another patient with incomplete laboratory data was excluded from further analysis. Among the 38 patients with active AOSD, 37 (97.4%), 31 (81.6%), 30 (78.9%), 24 (63.2%), 20 (52.6%) and 14 (36.8%) patients, respectively spike fever (

39°C), rash, arthralgia or arthritis, sore throat, liver dysfunction and lymphadenopathy. As shown in Table 3, there were no significant differences in entry age or proportion of females between AOSD patients and healthy controls (HC). Twenty-seven patients had the systemic subtype and the others had the chronic joint subtype.

Table 3. Demographics and laboratory findings of adult-onset Still's disease (AOSD) and healthy controls (HC) patients#.

Expression characteristics and cytokine profiles of LncRNAs in AOSD and HC patients

By qRT-PCR, the expression levels of six lncRNAΔCT ( ^CTlncRNA - ^CTGAPDH ) in each individual in AOSD systemic subtype, AOSD articulating subtype and healthy controls (HC) were shown, and the results are shown in Fig. 1a-1f. The mean ΔCT level of MIAT appeared to be lower for both AOSD subtypes compared to the median of HC (Fig. 1a), while the mean THRILΔCT trended higher for both AOSD subtypes (Fig. 1b) . However, none of the differences reached statistical significance as calculated by the Kruskal-Wallis test. Table 3 and Figure 1 show that the plasma levels of IL-1β, IL-6, IL-17A, IL-18 and TNF- α were significantly higher in AOSD patients than in HC. Notably, differences in IL-6, IL-18, and TNF-α levels between groups had lower p-values (p-values for IL-6, IL-18, and TNF-α were all <0.01).

Establishment of predictive scores using lncRNAs expression signatures and cytokines

To establish a diagnostic model for AOSD based on lncRNA expression, we further used the ΔCT values of MIAT and THRIL as variables to perform multiple regression analysis to predict the occurrence of AOSD. The coefficient for MIAT is -1.43, p=0.003, and the coefficient for THRIL is 1.62, p=0.001. We also performed multiple regression analysis using IL-6, IL-18 and TNF- α expression to predict AOSD, but only IL-18 expression reached statistical significance (p=0.027). The expression levels of MIAT, THRIL and IL-18 of each sample were projected onto a 3D scatterplot using R software. As shown in Figure 2a, AOSD samples could be best separated from control samples by a set of new putative biomarkers including expression of MIAT, THRIL and IL-18. In addition, 70% of the samples were randomly selected as the test dataset and the other 30% as the validation dataset to build a confusion matrix to evaluate the performance of the classification algorithm using this new set. Both the test dataset and the validation dataset were used to predict AOSD with 100% accuracy.

Therefore, based on the coefficients and values of MIAT, THRIL, and IL-18, we constructed prediction equations to calculate a score (referred to as "A-score") for diagnosing AOSD. The prediction equation is as follows: A-score = 1.62 * THRIL △CT - 1.43 * MIAT △CT + 0.02 * IL-18 (pg/ml)

ROC analysis for diagnosing AOSD with "A-score" showed an AUC of 0.998 at a cut-off value of 7.114, with a sensitivity of 94.87% and a specificity of 100% (Fig. 2b). For repeat testing in the second cohort, therefore, a further independent cohort sample of 16 patients with active AOSD was collected. Analysis using A-scores in the second AOSD patient cohort showed 100% classification accuracy at the same cutoff value (7.114).

Establishment of predictive scores for differentiating AOSD subtypes

To further differentiate AOSD subtypes of disease progression, different combinations of lncRNA expression and cytokine expression in AOSC patients in systemic and articular subtypes were plotted on a 3D scatterplot. The results showed that the expression of NTTΔCT and TNF-α best differentiated samples of the whole body subtype of AOSD from the samples of the joint subtype (Fig. 3a). The R generalized linear model with NTTΔCT and TNF-α expression as the confusion matrix to distinguish AOSD subtypes showed an accuracy of 76.92% on the test dataset (70% of all samples), while the accuracy on the validation dataset was 83.33% (30% of all samples).

A predictive equation consisting of the coefficients of the regression analysis and the amount of expression of NTT and TNF-[alpha] was established to calculate a score (referred to as "B-score") for determining the course of AOSD in a subject. The prediction formula is as follows: B-score = 0.22 * NTT △CT - 0.01 * TNF-α(pg/ml) .

ROC analysis of the "B-score" for discriminating AOSD subtypes showed an AUC of 0.855 at a cutoff value of 0.266, with a sensitivity of 66.7% and a specificity of 90.9% (Fig. 3h).

Correlation between lncRNA, cytokine expression and AOSD disease activity

Given that six lncRNAs were selected based on their functions in immune regulation and inflammatory responses, the correlation of lncRNA or cytokine expression with disease activity scores in AOSD patients was examined. Interestingly, the AOSD samples with the lowest expression of RMRP and PACERR (RMRPΔCT+PACERRΔCT>17) had higher disease activity scores (p=0.03, Figure 4a). Plasma IL-18 values were also strongly correlated with AOSD disease activity (r ² =0.38, p<0.0001, Figure 4b). However, the expression levels of NTT, NEAT1, MIAT, RMRP, HRIL and PACERR were not significantly correlated with the expression levels of plasma cytokines. In the systemic or articular subtypes of AOSD, the expression of each lncRNA did not significantly correlate with disease activity (Fig. 4c).

Changes of lncRNAs expression and proinflammatory cytokines in AOSD patients after treatment

Changes in the expression levels of 6 lncRNAs and the values of pro-inflammatory cytokines before treatment and 6-12 months after treatment in 10 treated AOSD patients were investigated. As shown in Figure 5a–b, NEAT1 expression was elevated after treatment with csDMARDs alone; whereas NEAT1 expression was observed to be decreased in AOSD patients treated with the IL-6 receptor inhibitor tocilizumab.

As PIK3CA may be a downstream regulator of lncRNAs revealed by network analysis (Fig. 6), a positive correlation between PIK3CA and NEAT1 was noted in post-treatment patients (Fig. 5c, r=0.89, p<0.01). The positive correlation of PIK3CA with the 6 lncRNAs as shown in Fig. 5d became more pronounced after treatment.

Expression patterns of LncRNAs in monocyte cell lines stimulated by TLR-ligands

Since activation of TLR3 has been reported to be associated with the pathogenesis of AOSD. Therefore, the lncRNA expression patterns after LPS stimulation of TLR3 ligand poly(I:C) and TLR4 ligand were tested in the human monocyte cell line THP-1. As shown in Figure 4, after 4 hours or 24 hours of stimulation, Poly(I:C) and LPS induced distinct lncRNA expression signatures. We observed an increase in MIAT expression and a decrease in THRIL expression in THP-1 cells stimulated by poly(I:C), and these results were similar to changes in lncRNA expression patterns in AOSD patients. Further, decreased expression of MIAT combined with increased expression of NEAT1 and RMRP was detected in LPS-treated THP-1 cells. In addition, poly(I:C) increased NTT expression at both 4 and 24 h stimulation, whereas LPS also increased NTT expression after 24 h stimulation (Fig. 7a). In addition, the results showed that stimulation with poly(I:C) increased PIK3CA expression in THP-1 cells, but not in LPS-stimulated cells (Fig. 7b).

Expression signature of MIAT, THRIL and IL-18 in the transcriptome of RA, SLE or sepsis

To investigate whether the lncRNA expression signatures identified in AOSD patients were similar to lncRNA expression patterns in other rheumatic diseases, the MetaSignature website (http://metasignature.stanford.edu/) was used to target data obtained from the Gene Expression Omnibus (GEO) Transcriptome analysis was performed on the transcriptome data in RA and SLE. An increase in MIAT and IL-18 and a decrease in THRIL expression were observed in PBMCs of AOSD patients. While THRIL values are not available on RA or SLE transcriptome arrays. However, a meta-analysis of MIAT and IL-18 expression on the RA and SLE transcriptome datasets showed that RA and SLE had different characteristics compared to the AOSD results. In RA patients, the expression levels of MIAT and IL-18 were not significantly increased (Fig. 8a-8b). In SLE patients, however, the expression of MIAT decreased, while the expression of IL-18 increased (Figures 8c-8d).

In addition, an additional 20 SLE and 20 RA patients were recruited to investigate the expression levels and pro-inflammatory cytokines of these 6 lncRNAs. The results show that by combining THRIL, MIAT and IL-18 expression were plotted on 3D scatter plots and principal component analysis (PCA) plots, which could separate AOSD from RA and SLE (Figures 9a-9b).

Tests using A-scores also differentiated RA and SLE patients from the AOSD group, with AUCs of 0.829 and 0.881, respectively, and higher cutoff values for detecting RA and SLE (12.57 and 11.39, respectively) (Figures 10a-10b). Given that no blood samples were available from sepsis patients, microarray data from the sepsis cohort GSE28750 (from NCBI's GEO database) were reanalyzed. In GSE28750, the expression data of lncRNA MIAT could be retrieved, but the value of THRIL could not be retrieved. The results showed that the relative expression of MIAT was significantly higher in the sepsis blood samples than in the controls, unlike the results in AOSD, RA or SLE (Fig. 1a and Figs. 9c-9e). Furthermore, PIK3CA expression was assessed in healthy or patients with AOSD, RA, SLE or sepsis (Tables 4 and 5). The results showed that PIK3CA expression was significantly higher in untreated AOSD patients compared with healthy controls (Fig. 10c), suggesting a potential pathogenic role of PIK3CA in AOSD. Conversely, PIK3CA expression was lower in RA and SLE patients compared with healthy controls, but there was no significant difference in PIK3CA expression in sepsis patients. Interestingly, PIK3CA expression was only positively correlated with MIAT expression in AOSD patients, but not in RA, SLE or sepsis (Fig. 10d).

Table 4. PIK3CA expression (ΔCT) in healthy or patients with AOSD, RA or SLE.

Table 5. Relative expression of PIK3CA to healthy and sepsis patients.

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Claims (8)

A method of detecting that an individual suffers from adult-onset Stubbs' disease, the method comprising the steps of: (a) providing a blood sample and a peripheral blood mononuclear cell; (b) detecting a protein biomarker in the blood sample Expression level, wherein the protein biomarker is IL-18; detecting the expression level of a long-chain non-coding RNA biomarker in the peripheral blood mononuclear cells, wherein the long-chain non-coding RNA biomarker is MIAT or THRIL; (c) calculating an A-score by applying a predictive function to the detected expression levels of IL-18, MIAT and THRIL obtained in step (b), wherein the predictive function is performed by a multiple regression analysis (multiple regression analysis) regression analysis); and (d) comparing the A-score calculated in step (c) with a threshold, wherein the threshold is obtained by a receiver operator characteristic (ROC) method, wherein the threshold The value corresponds to the area under the ROC curve (the area under an ROC curve, AUC) reaching the maximum value, wherein the A-score higher than the critical value represents that the individual suffers from adult-onset Stubbs disease, wherein the prediction function is A-score=1.62*THRILΔCT-1.43*MIATΔCT+0.02*IL-18(pg/ml). The method as described in item 1 of the claimed scope, wherein the threshold range is between 5.85 and 7.12. According to the method described in item 1 of the scope of the application, the expression level of a PIK3CA gene in the peripheral blood mononuclear cells is further detected, and when the expression level of the PIK3CA gene is higher than that of a healthy individual, the individual is excluded from systemic erythema Lupus, rheumatoid arthritis, or sepsis. The method of claim 1, wherein the healthy individual system is an individual who does not suffer from a rheumatic disease. The method of claim 1, wherein the blood sample is whole blood, serum or plasma. A method of detecting the disease course of an individual suffering from adult-onset Stubbs disease, the method comprising the steps of: (a) providing a blood sample and a peripheral blood mononuclear cell, wherein the blood sample and the peripheral blood mononuclear cell The nuclear cell line is collected from an individual with adult-onset Stilt's disease; (b) the blood sample is detected for the expression of a protein biomarker, wherein the protein biomarker is TNF-α ; detect the expression of a long-chain non-coding ribonucleic acid biomarker in the peripheral blood mononuclear cells, wherein the long-chain non-coding ribonucleic acid biomarker is NTT; (c) calculating a B-score using a predictive function of the detected expression levels of TNF-α and NTT obtained in step (b), wherein the predictive function is obtained by a multiple regression analysis; and (d) comparing the B-score calculated in step (c) with a threshold, wherein the threshold is obtained by a receiver operator characteristic (ROC) method, wherein the threshold corresponds to under the ROC curve The area under an ROC curve (AUC) reaches the maximum value, where the B-score higher than the cut-off value indicates that the disease course of the individual with adult-onset Stubbs is a systemic subtype or a joint inflammatory subtype, wherein the individual system of the adult-onset Stubbs syndrome is determined by the method of claim 1 of the scope of application, and the prediction function is B-score=0.22 * NTT △CT-0.01 * TNF- alpha (pg/ml). The method as described in item 6 of the claimed scope, wherein the threshold range is between 0.17 and 0.83. The method of claim 6, wherein the blood sample is whole blood, serum or plasma.

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