Circular RNA FUNDC1 improves prediction of stroke associated infection in acute ischemic stroke patients with high risk. Issue 6 (18th June 2020)

• Record Type: Journal Article
• Authors: Zuo, Lei; Li, Cai; Zu, Juan; Yao, Honghong; Yan, Fuling
• Is Part Of: Bioscience reports. Volume 40:Issue 6(2020)
• Publication Date: 2020-06-18
• Subjects: acute ischemic stroke -- biomarker -- circular RNA -- risk models -- stroke associated infection; Molecular biology -- Periodicals; Cytology -- Periodicals; 572.8
• DOI: 10.1042/BSR20200902
• Languages: English
• ISSNs: 0144-8463

Abstract

Abstract: Identifying those patients who were at high risk of stroke associated infection (SAI) for preventive antibiotic therapy was imperative for patients' benefits, thus improving prediction of SAI was critical for all acute ischemic stroke (AIS) patients. Circular RNA FUNDC1 (circFUNDC1) has been reported to be the diagnosis and prognosis biomarker of AIS. Therefore, the present study aimed to figure out whether circFUNDC1 could be the potential predictor of SAI that could help to guide preventive treatment. In total, 68 patients were included in the study, 26 of which had infection and 42 without. Copy number of circFUNDC1 in plasma were quantified by quantitative real-time polymerase chain reaction (qPCR). Platelet spike-in experiment and correlation analysis were conducted to explore possible origins of circFUNDC1 in plasma. A significantly elevated level of circFUNDC1 was found in SAI patients compared with not infected AIS patients ( P =0.0258). Receiver operating characteristic (ROC) curves demonstrated the prediction significance of circFUNDC1, with the area under the curve (AUC) at 0.6612 and sensitivity, specificity at 69.23%, 61.90% respectively in predicting SAI. Then, when adding circFUNDC1 in the risk model, the AUC increased from 0.7971 in model A to 0.8038 in model B. Additionally, positive correlation was observed between circFUNDC1 level and neutrophils counts. WBC and neutrophil ratios were significantly elevated in SAI patients compared with non-SAI patients. Therefore, circFUNDC1 could be used to construct a risk model for the prediction of SAI that is beneficial for AIS patients' preventive treatment.