Background Spondyloarthritis (SpA) and rheumatoid arthritis (RA) are chronic autoimmune diseases, but they are usually difficult to distinguish in the early stage of the diseases. The purpose of this study is to explore the differences of immune mechanism and diagnostic markers through bioinformatics analysis. Methods First, microarray datasets from patients with SpA, RA and normal controls were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between groups were identified in R software. Functional and pathway enrichment of DEGs were analyzed by David database. Then, we screened the hub genes using Cytoscape plugin, and constructed the protein–protein interaction (PPI) network and heatmap of hub genes. After that, CIBERSORT was used to evaluate the differences and connections of immune cells in SpA and RA, and screened out diagnostic markers. Correlation analysis was used to analyze the relationship between immune cells and diagnostic markers. Finally, quantitative real-time polymerase chain reaction (qRT‐PCR) was used to verify the effectiveness of immunodiagnostic markers. Results We obtained three datasets, from which we can see that the functional enrichment of DEGs is mainly in cell chemotaxis, lymphocyte activation, primary immunodeficiency and other immune responses. The difference of immune cells between SpA, RA and normal control was concentrated in B, T lymphocytes cells, macrophages and dendritic cells. C19orf12 + S1PR3 is most associated with these immune cells and S1PR3 can be used as a diagnostic marker of this kind of immune diseases. In addition, MZB1 + XIST is closely related to T cells, NK cells and dendritic cells, and is expected to be used as a marker to distinguish the two diseases. Conclusion Although the clinical manifestations of SpA and RA are similar, the pathogenesis is different. The screening of immune cells and diagnostic markers provides a more accurate target for the treatment of this kind of diseases.

Background Rheumatoid arthritis (RA) is a chronic systemic immune disease characterized by joint synovitis, but the specific etiology is unknown, and the characteristic serum diagnostic markers are also lacking. Methods First, we obtained the gene expression profile of synovium to evaluate the infiltration of immune cells in synovium, and screened the differentially expressed immune related genes for enrichment analysis. Subsequently, we classified RA into three subtypes by unsupervised clustering of serum gene expression profiles based on immune enrichment scores. Then, the enrichment and clinical characteristics of different subtypes were analyzed. Finally, according to the infiltration of different subtypes of immune cells, diagnostic markers were screened and verified by qRT-PCR. Results C1 subtype is related to the increase of neutrophils, CRP and ESR, and joint pain is more significant in patients. C2 subtype is related to the expression of CD8+T cells and Tregs, and patients have mild joint pain symptoms. The RF value of C3 subtype is higher, and the expression of various immune cells is increased. The function of this subtype is enriched in a variety of immune system diseases. T cells CD4, NK cells activated, macrophages M1 and neutrophils are immune cells significantly infiltrated in synovium and serum of RA patients. IFNGR1, TRAC, IFITM1 can be used as diagnostic markers of different subtypes. Conclusion In this study, RA patients were divided into different immune molecular subtypes based on gene expression profile, and immune diagnostic markers were screened, which provided a new idea for the diagnosis and treatment of RA.

Objective Free flaps are widely used for the repair of soft tissue defects in the lower limbs, but there is still a specific rate of necrosis. Few clinical retrospective studies have analyzed the nontechnical risk factors for lower limb free flap necrosis. This study aimed to analyze the nontechnical causes of flap necrosis in lower limb soft tissue reconstruction in order to identify risk factors and improve the survival rate of free flaps. Methods Clinical data from 244 cases of soft tissue defects of the leg or foot that were repaired with a free flap from January 2011 to June 2020 were retrospectively analyzed. The flap results were divided into complete survival and necrosis groups. The patients' general information, smoking history, soft tissue defect site, Gustilo‐Anderson classification, shock after injury, type and size of the flap, and time from injury to flap coverage were recorded. A logistic regression model was used to analyze the correlations between flap necrosis and possible risk factors. Results Of the 244 flaps, 32 suffered from partial or total necrosis, and 212 completely survived. Univariate analysis showed that age, smoking history, soft tissue defect site, and time from injury to flap coverage were significantly correlated with flap necrosis (p ≤ 0.2). Multivariate logistic regression analysis showed that moderate‐to‐severe smoking history (p < 0.001, odds ratio [OR] = 10.259, 95% confidence interval [CI] = 2.886–36.468), proximal leg defect (p = 0.006, OR = 7.095, 95% CI = 1.731–29.089), and time from injury to flap coverage >7 days (p = 0.003, OR = 12.351, 95% CI = 2.343–65.099) were statistically significant risk factors for flap necrosis (p < 0.05), and age was excluded (p = 0.666; p = 0.924). Conclusion The risk of flap necrosis was significantly increased when the soft tissue defect was located in the proximal leg, the time from injury to flap coverage was >7 days, and the patient had a moderate‐to‐severe smoking history. These three risk factors have an increased influence on flap necrosis and have guiding significance in predicting flap prognosis.