Abstract Purpose This study aims to determine whether Jianpi Qingre Tongluo Decoction (JQP) alleviates ankylosing spondylitis (AS) inflammation via the NONHSAT227927.1/JAK2/STAT3 axis. Methods The effect of JQP on immune-inflammatory indicators in AS patients was explored through a combination of data mining, association rule analysis, and random walk model evaluation. Subsequently, network pharmacology and molecular docking were performed to screen out the potential signaling pathway. ELISA, PCR and wb were used to evaluate the effect of JQP on AS-FLS activity and inflammatory factors. The role of NONHSAT227927.1/JAK2/STAT3 combination in inflammation was studied by editing NONHSAT227927.1 and adding the JAK2/STAT3 inhibitor AG490. Involvement of the JAK2/STAT3 pathway was detected by PCR, WB, or immunofluorescence analysis. Results Retrospective data mining results show that JQP can effectively reduce the immune inflammatory response in AS patients. Through network pharmacology and molecular docking, it is speculated that JQP exerts its effect on AS through the JAK2/STAT3 pathway. Overexpression of NONHSAT227927.1 activated the JAK2/STAT3 pathway and promoted the expression of inflammatory factors, while serum containing JQP reversed the effects of NONHSAT227927.1 overexpression. NONHSAT227927.1 silencing inhibits the proliferation of AS-FLSs, inhibits the levels of inflammatory factors, and reduces the expression of JAK2/STAT3 protein. After adding the pathway blocker AG490, it was observed that the cell viability of AS-FLSs was reduced by inflammatory factors and the levels of JAK2/STAT3 were inhibited. , and overexpression of NONHSAT227927.1 can reverse this trend. Conclusions JQP exerted an anti-inflammatory effect on AS by inhibiting the NONHSAT227927.1/JAK2/STAT3 axis. Keywords: Jianpi Qingre Tongluo Decoction, Ankylosing spondylitis, lncRNA NONHSAT227927.1, JAK2/STAT3 pathway, Inflammation 1. Introduction Ankylosing spondylitis (AS) is a chronic rheumatic disorder with inflammatory low back pain as the most common presenting symptom, mainly affecting the sacroiliac joints and spine, but also concerning peripheral joints and other organs [[31]1,[32]2]. AS alters the biomechanical properties of the spine through chronic inflammatory responses, leading to unstable spine fractures after minor trauma [[33]3]. The synovium is a highly specialized tissue that can secrete joint synovial fluid to lubricate and protect articular cartilage. Synovial hyperplasia and bone erosion are the characteristic features of many inflammatory joint disease including AS [[34]4] Fibroblast-like synoviocytes (FLSs), a heterogenous cell population of the synovium, contribute actively to inflammatory responses and play a decisive role in the pathophysiological process of AS. FLSs secrete a variety of inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) [[35]5] and interleukin-6 (IL-6) to trigger and exacerbate inflammatory signaling cascades [[36]6,[37]7], leading to local inflammation and joint destruction [[38]8]. At present, the main goals of AS management are to control inflammation, promote functional recovery, and reduce deformity [[39]9]. Despite the favorable overall efficacy of the available AS treatment regimens, the clinical symptoms of many patients have not achieved acceptable improvement, and pain still severely compromises the health and daily life of patients. Therefore, developing new drugs to effectively relieve AS symptoms remains an urgent issue to be solved. Encouragingly, traditional Chinese medicine has shown great potential in the treatment of AS. Domestic experts have basically reached a consensus that traditional Chinese medicine can be applied to the clinical treatment of AS [[40]9,[41]10]. TCM has potential values in treating AS. In line with the tenets of traditional Chinese medicine, AS belongs to the category of “Bi Syndrome.” Chinese clinicians are more inclined to a combination of traditional Chinese and Western medicine to provide personalized treatment for patients based on syndrome differentiation. The traditional Chinese medicine compounds Xinfeng Capsule (XFC, patent number ZL201310011369.8) and Huangqin Qingre Chubi Capsule (HQC, patent number ZL201110095718.X) are prepared under the guidance of Xinan medical theory and combined to form Jianpi Qingre Tongluo Decoction (JQP). XFC was prepared from Huang Qi (Astragalus flavone; A. membranaceus (Fisch.) Bge., root), Yi Yi Ren (coix seed; Coix lacryma-jobi L.var.mayuen (Roman.) Stapf, kernel), Lei Gong Teng (tripterigium wilfordii; Tripterygium wilfordii Hook. f.,root),Wu Gong (centipede; Scolopendridae, Dried body) at a ratio of 20:20:20:1 [[42]11]. The content and stability of the four core chemical components of traditional Chinese medicine in XFC were analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry. Through testing, the method of reproduction of astragaloside IV, formononetin, and verbascoside was established, and a directional quantitative analysis method was established [[43]12]. HQC is prepared from Huang Qin (s.baicalensis; Scutellaria baicalensis Georgi; root); Wei Ling Xian (chinese clematis root; Clematis chinensis Osbeck; root); Zhi Zi(gardenia jasminoides; Gardenia jasminoides J. Ellis; fruit) Yi Yi Ren (coix seed; Coix lacryma-jobi L.var.mayuen (Roman.) Stapf, kernel); Tao Ren (peach seed; Prunusdavidiana (Carr.)Franch; kernel) in a ratio of 10:10:9:30:5. The six core chemical components of traditional Chinese medicine in HQC were identified through fingerprints, proving that baicalin, gardeniposide, oleanolic acid, amygdalin, and coixin The content is relatively high, and the similarity between the HQC fingerprint and the monomer reference substance is above 0.9, indicating that the quality of HQC used in clinical applications is stable [[44]13]. Western medical treatment has the advantages of rapid onset of action and significant improvement in indicators, but has unavoidable drawbacks, such as prolonged use of hormones that may lead to necrosis of the femoral head [[45]14], decreased immunity, and increased risk of infections [[46]15], gastrointestinal problems and hepatotoxicity associated with nonsteroidal anti-inflammatory drugs (NSAIDs), the high price of biologics. Therefore, TCM as a complementary therapy can achieve the goal of reducing toxicity, improving therapeutic efficacy as well as enhancing synergistic effects. JQP has been used for the treatment of damp-heat syndrome of AS for more than 20 years, showing remarkable therapeutic effects. The HPLC-generated fingerprint has verified JQP's safety, stability, and efficacy in medical treatments [[47]16,[48]17]. JQP contributes to reducing AS inflammation and redox reactions [[49]18], improving bone metabolism, relieving extra-articular symptoms, inhibiting platelet activation [[50]19], and improving cardiac function [[51]20]. Moreover, JQP can also notably alleviate the anxiety and depression state of AS patients. Long non-coding RNAs (LncRNAs) are generally defined as non-coding RNA transcripts with a transcript length of more than 200 nucleotides, which are extensively implicated in a variety of biological processes [[52]21,[53]22]. Aberrant lncRNA expression pattern has been associated with the pathogenesis of AS [[54]23]. For example, up-regulation of lncRNA NEF is indicative of AS recurrence and dismal outcomes [[55]24]. LncRNA MEG3 is down-regulated in AS and holds predictive value concerning disease duration, disease severity, and length of hospital stay [[56]25]. LncRNA MEG3 subsides AS inflammation partially by targeting miR-146a [[57]26]. High-throughput sequencing and bioinformatics analysis previously conducted by our team has revealed the involvement of lncRNA NONHSAT227927.1 in AS inflammation. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway is a common pathway of cytokine signal transductions, and JAK/STAT pathway dysregulation precedes numerous inflammatory and autoimmune diseases [[58]27,[59]28]. Pro-inflammatory cytokine activation of the JAK/STAT pathway is a critical event in the initiation and progression of AS. For example, it is reported that the JAK/STAT signaling cascade plays a potential role in the pathogenesis of AS by regulating the activation and proliferation of IL17+ effector memory T cells [[60]29,[61]30]. Among different JAK/STAT isoforms, JAK2/STAT3 has been studied most intensively. Accordingly, we speculated that AS inflammation could be partially attributed to the dysregulation of the JAK2/STAT3 pathway. In this study, we validated the beneficial impact of JQP on immune and inflammatory markers in AS patients in a laboratory setting. Additionally, our in vitro experiments supported the notion that JQP may suppress AS inflammation by modulating the NONHSAT227927.1/JAK2/STAT3 pathway. These findings contribute to the potential therapeutic implications of JQP for ankylosing spondylitis. ([62]Fig. 1). Fig. 1. [63]Fig. 1 [64]Open in a new tab Flow chart of study. 2. Materials and methods 2.1. Drug source JQP consists of XFC and HQC, XFC, patent No. ZL201310011369.8, Approval number Z20050062; HQC, patent No. ZL201110095718.X, Approval number Z20200001, Both drugs were purchased from Anhui Provincial Hospital of Traditional Chinese Medicine. 2.2. Clinical data for diagnosis of AS The data for inpatient AS patients in the Department of Rheumatology from June 2012 to June 2021 was sourced from the Information System Database of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine [[65]31]. Exclusion criteria: incomplete data, severe diseases affecting immune globulin and complement such as diseases of the circulatory system, blood system, and respiratory system, pregnancy, and lactation. Non-steroidal anti-inflammatory drugs and other fundamental treatments were regularly administered to all the patients included. The clinical data that was gathered encompassed age, gender, disease progression, and clinical laboratory markers. The clinical laboratory indicators included erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), immunoglobulin A (IgA), immunoglobulin M (IgM), immunoglobulin G (IgG), complement component 3 (C3), and complement component 4 (C4). After screening and deduplication, After collecting the clinical data of 705 AS patients, they were split into the JQP group and the non-JQP group based on whether they had been given JQP treatment. Out of the total, 263 individuals were given JQP treatment while 4423 were not given JQP treatment (Review number: 2020AH-208). 2.3. Association rule analysis An Apriori algorithm-based association rule analysis was performed for mining association relationships among items. JQP and laboratory parameters were used as variables. The use of JQP in AS patients was scored as 1, and 0 otherwise; any improvement in laboratory parameters was assigned as 1, and 0 otherwise, with minimum support of 30 %, minimum confidence of 50 %, and a lift of >1. 2.4. Random walk model for laboratory indicators The ORACLE 10 g tool was employed to assess the random walk model of immune-inflammatory indicators between the two groups in order to measure the effectiveness of JQP treatment on laboratory indicators in AS patients numerically. Randomized stroll models can be used to determine whether JQP treatment is associated with improved immune inflammatory markers in the long run, and to determine improvements in overall patient outcomes. 2.5. Acquisition of active ingredients, potential targets, and signaling pathways The active ingredients and corresponding targets of each TCM in the core prescription were screened from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database ([66]https://tcmspw.com) by the criteria of drug likeness (DL) ≥ 0.18 and oral availability (OB) ≥ 30 %. Gene/pathway-protein data were obtained via UniProt ([67]https://www.uniprot.org/) with conditions set to Popular Organism: Human and Status: Review (Swiss-Prot). Through integration, the information of active ingredients-target genes-pathways was finally obtained. With “ankylosing spondylitis” as keywords, AS-related target genes were obtained from 5 disease databases including Therapeutic Target Database ([68]http://db.idrblab.net/ttd/), Human Online Mendelian Inheritance ([69]https://www.omim.org/), Drug Database ([70]https://www. drugbank.ca/), Gene Cards ([71]https://www.genecards.org/), and DisGeNET ([72]https://www.disgenet.org/). 2.6. Construction of PPI network Using the Search Tool for the Retrieval of Interacting Genes (STRING) and setting the species to “Homo sapiens” with a minimum interaction score >0.4, the PPI network was constructed. For PPI network visualization, the analysis results were installed in Cytoscape 3.7.2 after being exported to a “TSV” format file. For maximum group centrality (MCC) screening, the degree value is utilized, and the top 20 target genes were chosen as core target genes. 2.7. KEGG analysis The potential target genes were imported into Metascape ([73]https://metascape.org/) for customized analysis, and the species was limited to “Homo sapiens”. The P-value cutoff for KEGG analysis was set at 0.05. Bioinformatics ([74]http://www.bioinformatics.com.cn/), an online bioinformatic analysis and visualization cloud platform, was used for visualization. 2.8. Preparation of JQP-medicated serum After a week of adaptive feeding, 20 male Sprague-Dawley (SD) rats, specifically pathogen-free and weighing 180–220 g (Lscxk 2017-001, Experimental Animal Center of Anhui Medical University), were randomly assigned to the usual group and the JQP group. JQP (32.4 mg/100g) was administered intragastrically at a dose of 20 times the clinical equivalent, which was calculated using a conversion coefficient based on body surface area from human to rat. For seven days in a row, the same amount of solvent was administered once daily to the usual group. Rats' abdominal aortas were used to harvest blood 1 h following the previous injection. The blood was then centrifuged for 10 min at 3000 r/min to extract serum, and the samples were kept in a water bath at 56 °C for 30 min for inactivation, separated at −80 °C for aseptic operation, and stored for use. The study adheres to the 3R guidelines, namely Replacement, Reduction and Refinement. This study was approved by the Experimental Animal Ethics Committee of Anhui University of Traditional Chinese Medicine (Review number: AHUCM-rats-2023020). 2.9. Molecular docking of JQP core components with STAT3 The common targets of JQP intervention in AS disease were imported into Cytoscape 3.7.2 to construct a “drug-active ingredient-target” network. The core components of JQP were sorted according to the degree value, and the top three components (quercetin, kaempferol, and wogonin) were selected for molecular docking with the pathway proteins. The Protein Data Bank (PDB) format file of the STAT3 protein was searched in the PDB database ([75]https://www.rcsb.org/), and the Mol2 format file of the structure of quercetin, kaempferol, and wogonin was downloaded from the TCMSP database. Before molecular docking, the target protein receptor molecules were dehydrated and the ligand small molecules were removed using PyMOL software. The target protein was hydrogenated using Auto Dock 4.2.6 software. Finally, the receptor proteins were molecularly docked with ligand small molecules using Auto Dock Vina 1.1.2 software and visualized using PyMOL 2.3.0 software. The binding energy between receptor proteins and ligand small molecules was less than −5 kcal/mol, indicating a strong binding force. 2.10. Cultivation of AS-FLSs Purchased from Subikang iCell Bioscience Inc. (Shanghai, China), AS-FLSs (NO: JDBG200752) were cultured at 37 °C with 5 % CO2 in a standard cell culture medium supplemented with 10 % fetal bovine serum, 100 IU/ml penicillin, and 100 IU/ml streptomycin. Twice a week, the medium was changed until a sufficient number of cells were present. The cultures were then separated and kept in storage at −80 °C. 2.11. Reagent handling and cell transfection The JAK2/STAT3 pathway inhibitor AG-490 (10 μm) (MCE, No.: HY-12000) was applied to AS-FLSs for a duration of 24 h. To overexpress NONHSAT227927.1, the NONHSAT227927.1 gene was introduced into the pcDNA3.1 plasmid (GenePharma, Shanghai, China). Additionally, an empty pcDNA3.1 vector was created. Furthermore, three si-RNA sequences that target NONHSAT227927.1 were created in order to mute the latter. The following primers were utilized: si-RNA-NC (forward sequence: 5′-UUCUCCGAACGUGUCACGUTT-3′ and reverse sequence: 5′-ACGUGACACGUUCGGAGAATT-3′), si-NONHSAT227927.1.1 (forward sequence: 5′-CGACUGACUCGAUCUUUGAAG-3′ and reverse sequence: 5′-ACGUGACACGUUCGGAGAATT-3′), si-NONHSAT227927.1.2. (forward sequence: 5′-CGACUGACUCGAUCUUUGAAG-3′ and reverse sequence: 5′-UCAAAGAUCGAGUCAGUCGGG-3′), and si-NONHSAT227927.1.3. (forward sequence: 5′-AGAGGUGUUCAUUAUGGAAUC-3′ and reverse sequence: 5′-UUCCAUAAUGAACACCUCUGA-3′). Cell transfection was performed using Lipofectamine 2000 reagents (Invitrogen, Carlsbad, California, USA). The cells were harvested for the following detection 48 h after the transfection. 2.12. Enzyme-linked immunosorbent assay (ELISA) AS-FLS culture supernatant was collected, seeded into a microtiter plate (100 μL per well), and incubated at 37 °C for 1.5 h. The levels of IL-6 (JYM0140Hu), IL-17 (JYM0082Hu), IL-10 (JYM0155Hu), and TNF-α (JYM0110Hu) were found in complete accordance with the ELISA kits' recommendations. Using a microplate reader, the optical density (OD) at 490 nm was determined. Every sample underwent three independent tests. 2.13. Real-time quantitative polymerase chain reaction (RT-qPCR) The RNAiso Plus Reagent (TaKaRa) was utilized to extract total RNA. β-actin was used as the internal control when creating the cDNAs using the PrimeScriptTMRT Reagent Kit with gDNA Eraser (TaKaRa, Japan). To determine the relative expression values, the 2-ΔΔCt method was used. 2.14. Cell counting kit-8 (CCK-8) assay The CCK-8 assay was used to detect the effect of different concentrations of JQP on the viability of AS-FLSs at 24, 48, and 72 h. AS-FLSs were digested with trypsin, and 100 μL cell suspension was added to each well of a 96-well culture plate. Upon reaching 80 % confluence, the cells were supplemented with the pre-prepared JQP-containing serum at different concentrations and incubated for 12, 24, 48, and 72 h. Thereafter, 10 μL CCK-8 solution was added into each well for another 1.5-h incubation. The absorbance of each well at 450 nm was detected using a microplate reader to evaluate the effect of different concentrations of JQP on the viability of AS-FLSs. 2.15. Western blot (WB) The cells were subjected to radio-immunoprecipitation assay cell lysis buffer to extract the total protein, which was then quantified, loaded with samples, electrophoresis, and transferred to the membrane. The membrane was treated with 5 % skim milk at room temperature for 2 h, followed by an overnight incubation at 4 °C with primary antibodies against phosphorylation p-STAT3 (1:1000), p-JAK2 (1:500), JAK2 (1:500), and STAT3 (1:1000). Subsequently, the membrane underwent incubation with the horseradish peroxidase-labeled secondary antibody (diluted at a 1:1 × 104 ratio) at ambient temperature for a duration of 2 h. All protein bands were observed through electrochemiluminescence. The relative expressions of target proteins were computed by dividing the absorbance of LaminB1 by that of glyceraldehyde-3-phosphate dehydrogenase. 2.16. Statistical analysis This study adopted a retrospective self-controlled case series design. Data were analyzed using SPSS 22.0 software (IBM Corp., USA) and expressed by median and quartile (P25, P75). Categorical variables (gender) were compared using the chi-square test. The paired t-test or Kruskal-Wallis nonparametric test was used to evaluate the significance of differences between the two groups. A value of P < 0.05 was indicative of significant differences. The plant names mentioned in the article have been checked through World Floraonline ([76]www.worldfloraonline. org). Map plotting was performed with Graphpad Prism 8.0, and puzzles were made with Adobe Illustrator 2022. 3. Results 3.1. Changes of laboratory parameters in AS patients after JQP treatment The data of 705 AS patients admitted to the Rheumatology Department of Anhui Provincial Hospital of Traditional Chinese Medicine between June 2012 and June 2020 was gathered. The JQP group and the non-JQP group were the two groups to which these patients were allocated. The patient's baseline data underwent Propensity Score Matching (PSM) in a 1:1 ratio to mitigate any potential selection biases. The volume difference that was matched was set to 0.01, and the fixed covariates were age, gender, disease duration, number of hospitalizations, and clinical laboratory parameters. After PSM, 526 AS patients were included in the study, with 263 from the JQP group and 263 from the non-JQP group ([77]Table 1). Following the comparison, there was no significant disparity in the initial demographic attributes and clinical and laboratory parameters between the two cohorts. Table 1. Baseline matching of AS patients. __________________________________________________________________ Before PSM __________________________________________________________________ __________________________________________________________________ After PSM __________________________________________________________________ __________________________________________________________________ JQP group (n = 263) Non-JQP group (n = 442) P JQP group (n = 263) Non-JQP group (n = 263) P Age 43 (34,52) 37 (30,48) <0.01 43 (34,52) 41 (31,52) 0.54 Gender, n Male 70 125 0.62 70 70 1 Female 193 317 193 193 Disease duration, years 9 (8,11) 8 (7,10) <0.01 9 (8,11) 9 (7.50,10.00) 0.06 ESR, mm/h 25 (14,43) 26 (14,54) 0.35 25 (14,43) 26 (13,51) 0.47 CRP, mm/L 18.87 (5.91,37.55) 9.83 (1.39,31.47) 0 18.87 (5.91,37.55) 11.24 (1.50,34.67) 0.65 IgA, mmol/L 2.42 (1.92,3.22) 2.1 (1.65,2.80) 0.01 2.42 (1.92,3.23) 2.27 (1.79,3.01) 0.23 IgG, mmol/L 12.6 (10.50,15.23) 11.7 (9.87,14.18) 0.87 12.6 (10.50,15.26) 12.07 (10.50,15.01) 0.52 IgM, mmol/L 1.03 (0.80,1.39) 1.11 (0.80,1.48) <0.01 1.03 (0.80,1.39) 1.09 (0.80,1.46) 0.14 C3, g/L 1.24 (1.08,1.40) 1.17 (1.05,1.35) <0.01 1.24 (1.08,1.40) 1.18 (1.03,1.36) 0.23 C4, g/L 0.31 (0.26,0.36) 0.29 (0.26,0.35) <0.01 0.31 (0.26,0.36) 0.28 (0.24,0.34) 0.63 [78]Open in a new tab Notes:: Chi-square test was used for gender comparison, and Mann-Whitney nonparametric test was used for age, disease duration, and laboratory parameter comparisons. 3.2. Effect of JQP on clinical laboratory parameters in patients with AS after PSM The levels of ESR, CRP, IgA and C4 in the JQP group were decreased after treatment compared with those before treatment (P < 0.01). The levels of IgG, IgM, and C3 in the JQP group were not statistically significant before and after treatment (P > 0.05). The levels of ESR, CRP, IgA and IgG in the non-JQP group were decreased significantly after treatment (P < 0.01), but the changes in the levels of IgM, C3 and C4 were not statistically significant. In addition, Comparing the two groups before and after treatment, the ESR, CRP and IgA indicators of the JQP group after treatment were significantly improved compared with the non-JQP group. The non- JQP group improved C4 better than the JQP group, suggesting that JQP has advantages over the non-JQP group in improving AS immune and inflammatory indicators.(P < 0.05; [79]Table 2). Table 2. Effect of JQP on clinical laboratory parameters of AS patients. __________________________________________________________________ JQP group (n = 263) __________________________________________________________________ __________________________________________________________________ Non-JQP group (n = 263) __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ Before treatment After treatment Difference Before treatment After treatment Difference P ESR, mm/h 25 (14,43) 12(7.21) 7(0,22)[80]^c 26 (13,51) 19 (11,34) 1(0,14)[81]^b <0.01 CRP, mg/L 18.87 (5.91,37.55) 5.74(2.04,14.28) 7.25(0.78,22.83)[82]^c 11.24 (1.50,34.67) 1.94 (0.50,10.31) 2.96(0,21.67)[83]^b <0.01 IgA, mmol/L 2.42 (1.92,3.23) 2.22(1.71,3.10） 0(0,0.24)[84]^a 2.27 (1.79,3.01) 2.24 (1.72,2.98) 0(0,0.14)[85]^a <0.01 IgG, mmol/L 12.6 (10.50,15.26) 12.4 (10.30,15.00) 0(0,0.07) 12.07 (10.5,15.01) 11.9 (10.23,14.28) 0(0,0.01)[86]^a 0.08 IgM,mmol/L 1.03 (0.80,1.39) 1.04 (0.79,1.37) 0(0,0) 1.09 (0.80,1.46) 1.09 (0.77,1.46) 0(0,0.01) 0.53 C3, g/L 1.24 (1.08,1.40) 1.20 (1.06,1.35) 0(0,0.13) 1.18 (1.03,1.36) 1.14 (0.99,1.3) 0(0,0.09) 0.51 C4, g/L 0.31 (0.26,0.36) 0.26 (0.20,0.31) 0.03(0,0.09)[87]^b 0.28 (0.24,0.34) 0.27 (0.22,0.32) 0(0,0.04) <0.01 [88]Open in a new tab Notes. The P-value was the difference between the JQP group and the non-JQP group after treatment, and the Mann-Whitney nonparametric test was the statistical method used. ^a p < 0.05. ^b p < 0.01. ^c p < 0.001. 3.3. Examining the correlation between JQP and clinical laboratory indicators through association rule analysis The former item was set as JQP and the latter item as laboratory indicator, with a minimum confidence of 55 % and a minimum support of 30 %. The Apriori algorithm-based association rule analysis showed that JQP improved the confidence level of ESR to 81.89 %, CRP to 70.94 %, IgG to 69.81 %, and C4 to 60.75 %. The improvement degree of the above indicators was all greater than 1 ([89]Fig. 2). These results suggested that JQP was closely associated with the improvement of immune-inflammatory indicators in AS patients. Fig. 2. [90]Fig. 2 [91]Open in a new tab Examining the correlation between JQP and clinical laboratory indicators through association rule analysis. 3.4. Random walking evaluation model of laboratory indicators after JQP treatment Random walking was performed for the indicators with statistical differences before and after treatment in the JQP group, and the random walk steps of ESR, CRP, and C4 were 48, 63, and 18, respectively. The random positive growth rates of ESR, CRP, and C4 were 0.24 ± 0.10, 0.31 ± 0.14, and 0.33 ± 0.12, respectively. The number of comprehensive evaluation records for ESR, CRP, and C4 were 114, 319, and 313, respectively ([92]Fig. 3). The clinical significance of the above data indicates that every time the comprehensive evaluation of ESR, CRP, and C4 indicators is improved, the patient needs to take 11.74, 9.42, and 4.64 steps, respectively. Fig. 3. [93]Fig. 3 [94]Open in a new tab Random walk model of inflammatory indicators in AS patients treated with JQP. Note: Time changes are indicated by the horizontal line's increasing length as the number of walks increases. As the intervention bears effect, the vertical line's height rises, signifying that the indicator is becoming healthier. Network pharmacology and molecular docking of the main components of JQP with STAT3 protein molecule. From TCMSP database, coix seed (Yi Yi Ren: YYR), Astragalus flavone (Huang Qi: HQ), tripterygium wilfordii (Lei Gong Teng: LGT), centipede (Wu Gong: WG), gardenia jasminoides (Zhi Zi:ZZ), Chinese clematis root (Wei Lin Xian:WLX), s.baicalensis(HuangQin:HQ) and peach kernel (Tao Reng:TR) were retrieved from the database. 163 active compounds and 218 potential target compounds were obtained. Through the screening of genecards database and OMIM database, 2073 as targets were obtained. By examining the intersection of drug and disease targets, 69 overlapping genes were identified ([95]Fig. 4A). The above results show that these 69 Chinese medicine candidate targets are involved in the occurrence process of AS ([96]Fig. 4B). They were screened by degree value through the PPI network, and the top 20 proteins were finally screened out ([97]Fig. 4C), which are the targets of Chinese medicine against AS immune inflammation. In addition, we used Cytoscape 3.7.2 software to draw the drug active ingredient-target gene network ([98]Fig. 4D), and KEGG further analyzed 69 candidate targets. The results showed that the JAK2/STAT3 pathway related to AS was involved in the top 20 candidate targets ([99]Fig. 4E). Fig. 4. [100]Fig. 4 [101]Open in a new tab Network pharmacology and molecular docking of the main components of JQP with the STAT3 protein molecule. A. Venn diagram of candidate target genes of JQP and AS. B. Protein-protein interaction network, the darker the color, the higher the importance. C. Degree screening of the top 20 gene targets. D. Cytoscape 3.7.1 The software draws the active ingredient-target gene network of TCM, in which the yellow square represents the target gene, the green circle represents the traditional Chinese medicine, and the green square represents the active ingredient of the traditional Chinese medicine. E. Bubble chart of KEGG pathway enrichment analysis of candidate AS targets. The size of the dots indicates the number of selected genes, and the color indicates the p value of the enrichment analysis. F. Visualization of molecular docking of active ingredients and core protein targets in the core formulation. (a) Docking diagram of quercetin (MOL000098) and STAT3. (b) Docking diagram of kaempferol (MOL000422) and STAT3. (c) Docking diagram of wogonin (MOL000173) and STAT3. (For interpretation of the references to