Using the co-expression network of T cell-activation-related genes to assess the disease activity in Takayasu’s arteritis patients

• Published in: Arthritis research & therapy Vol. 23; no. 1; pp. 303 - 16
• Main Authors: Tian, Yixiao, Li, Jing, Tian, Xinping, Zeng, Xiaofeng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 16.12.2021; BioMed Central; BMC
• Subjects: Arthritis; Biomarkers; Diagnosis; Disease activity; Gene expression; Genetic aspects; Health aspects; Humans; Lymphocyte Activation - genetics; Mathematical models; Medical diagnosis; mRNA; Patients; Physiological aspects; Remission (Medicine); Risk factors; T cell activation; T cells; T-Lymphocytes - metabolism; Takayasu Arteritis - genetics; Takayasu's arteritis; Tumor necrosis factor-TNF; Vein & artery diseases; China; mRNA; Disease activity; Diagnosis; T cell activation; Takayasu’s arteritis
• DOI: 10.1186/s13075-021-02636-2
• ISSN: 1478-6362; 1478-6354

There have been lacking reliable serum biomarkers in assessing the disease activity of Takayasu's arteritis (TAK). This study aimed to assess the disease activity of TAK by assayed gene expression levels in peripheral mononuclear cells (PBMCs). The expression level of genes that essential in T cell activation in PBMCs in active TAK patients, inactive TAK patients, and healthy controls were detected by real-time fluorescence quantitative polymerase chain reaction, including TCR, CD28, CD40, CD40L, PD-1, PD-L1, PD-L2, CTLA4, TIGIT, TIM3, LAG3, CCL5, T-bet, RORC, and FOXP3. Gene co-expression network was established, and the signature of the topology structure in active TAK patients compared to the inactive TAK patients were extracted and described by formulas. Respectively, the disease activity was assessed by the routine serum biomarkers, including ESR, CRP, IL-6, and TNF-α, the gene expression level of TCR, CD28, T-bet, and RORC, as well as the signature of the topology structure, and the diagnostic efficacies were compared. Compared with the inactive TAK patient group, the active TAK patient group had a greater clustering coefficient in the network consisting of genes that essential in T cell activation. When assessing the disease activity used this signature of topology structure, the sensitivity was 90.9%, the specificity was 100%, and the AUC was 0.98, which was greater than the AUCs of these biomarkers. The signature of the topology structure could distinguish the active TAK patients from inactive TAK patients. This maybe is a novel evaluation algorithm of disease activity.