This study aims to explore the predictive noninvasive biomarker for obstructive coronary artery disease (CAD). By using the data set GSE90074, weighted gene co‐expression network analysis (WGCNA), and protein–protein interactive network, construction of differentially expressed genes in peripheral blood mononuclear cells was conducted to identify the most significant gene clusters associated with obstructive CAD. Univariate and multivariate stepwise logistic regression analyses and receiver operating characteristic analysis were used to predicate the diagnostic accuracy of biomarker candidates in the detection of obstructive CAD. Furthermore, functional prediction of candidate gene biomarkers was further confirmed in ST‐segment elevation myocardial infarction (STEMI) patients or stable CAD patients by using the datasets of GSE62646 and GSE59867. We found that the blue module discriminated by WGCNA contained 13 hub‐genes that could be independent risk factors for obstructive CAD (P < .05). Among these 13 hub‐genes, a four‐gene signature including neutrophil cytosol factor 2 (NCF2, P = .025), myosin‐If (MYO1F, P = .001), sphingosine‐1‐phosphate receptor 4 (S1PR4, P = .015), and ficolin‐1 (FCN1, P = .012) alone or combined with two risk factors (male sex and hyperlipidemia) may represent potential diagnostic biomarkers in obstructive CAD. Furthermore, the messenger RNA levels of NCF2, MYO1F, S1PR4, and FCN1 were higher in STEMI patients than that in stable CAD patients, although S1PR4 showed no statistical difference (P > .05). This four‐gene signature could also act as a prognostic biomarker to discriminate STEMI patients from stable CAD patients. These findings suggest a four‐gene signature (NCF2, MYO1F, S1PR4, and FCN1) alone or combined with two risk factors (male sex and hyperlipidemia) as a promising prognostic biomarker in the diagnosis of STEMI. Well‐designed cohort studies should be implemented to warrant the diagnostic value of these genes in clinical purpose.

To assess T cell subsets and levels of chemokines and cytokines in patients with SLE and determine their relationships between disease activity and organ involvement. Blood samples from SLE patients (n = 24) and healthy controls (n = 36) were analyzed. Frequency of circulating follicular help T cells (Tfh), central memory T cells (Tcm), effector memory T cells (Tem), and naïve T cell subsets was enumerated and their surface markers expression of inducible T cell co-stimulator (ICOS) and programmed death 1(PD-1) protein was examined by flow cytometry. The disease state in SLE patients was evaluated using the SLE Disease Activity Index (SLEDAI). Concentrations of autoantibodies, serum C-reactive protein (CRP), the erythrocyte sedimentation rate (ESR), lgG, complement 3, complement 4, cytokines, and chemokines, such as IL-21, IL-17A, and IL-1β, were measured. The frequencies of circulating Tfh and Tcm cell subsets were significantly lower than those in healthy controls. However, the percentages of circulating PD1ICOSTfh, PD1ICOSTcm, and PD1ICOSTem of PBMCs from SLE patients were higher than those in healthy controls. Furthermore, increased levels of serum IL-1β, IL-4, IL-6, MCP-1, IL-21, and IL-17A were detected in the patients with SLE compared to healthy controls. In addition, patients with immune thrombocytopenia displayed elevated proportions of serum IL-10, IL-17A, and IL-1β. Aberrant T cell subsets and cytokines expression profile were observed in SLE patients. PD1ICOSTem cell subset was clearly influenced by disease activity and serum IL-10, IL-17A, and IL-1β were significantly increased in patients with immune thrombocytopenia. Therefore, PD1ICOSTem cells might serve as an important tool for recognition and serum IL-10, IL-17A, and IL-1β might be an effective monitor for SLE patients with immune thrombocytopenia.

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