Yuanlong Li1, Hua Fan2, Jun Sun1, Ming Ni3, Lei Zhang1, Ci Chen1, Xuejiao Hong1, Fengqin Fang1, Wei Zhang1, Peizhi Ma4. 1. Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou, China; Department of Pharmacy, People's Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China; Department of Pharmacy, People's Hospital of Henan University, School of Clinical Medicine, Henan University, Zhengzhou, China. 2. The First Affiliated Hospital of Henan University of Science and Technology, School of Clinical Medicine, Henan University of Science and Technology, Luoyang, China. 3. Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou, China; Department of Clinical Pharmacy, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China. 4. Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou, China; Department of Pharmacy, People's Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China; Department of Pharmacy, People's Hospital of Henan University, School of Clinical Medicine, Henan University, Zhengzhou, China. Electronic address: liyuanlong011075@126.com.
Abstract
OBJECTIVE: To investigate circular RNA (circRNA) expression profile via microarray, and further assess the potential of candidate circRNAs as biomarkers in Alzheimer's disease (AD). METHODS: CircRNA expression profile in cerebrospinal fluid from 8 AD patients and 8 control (Ctrl) subjects was assessed by microarray. Subsequently, 10 candidate circRNAs from microarray were validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in cerebrospinal fluid from 80 AD patients and 40 Ctrl subjects. RESULTS: By microarray, 112 circRNAs were upregulated and 51 circRNAs were downregulated in AD patients compared with Ctrl subjects, and these circRNAs were enriched in AD related pathways such as neurotrophin signaling pathway, natural killer cell mediated cytotoxicity and cholinergic synapse. By RT-qPCR, circ-LPAR1, circ-AXL and circ-GPHN were increased, whereas circ-PCCA, circ-HAUS4, circ-KIF18B and circ-TTC39C were decreased in AD patients compared with Ctrl subjects, and these circRNAs were disclosed to predict AD risk by receiver operating characteristics curve analysis. Further forward-stepwise multivariate logistic regression revealed that circ-AXL, circ-GPHN, circ-ITPR3, circ-PCCA and cic-TTC39C were independent predictive factors for AD risk. Besides, in AD patients, circ-AXL and circ-GPHN negatively correlated, while circ-PCCA and circ-HAUS4 positively correlated with mini-mental state examination score; Circ-AXL negatively correlated, while circ-PCCA, circ-HAUS4 and circ-KIF18B positively correlated with Aβ42; Circ-AXL and circ-GPHN positively correlated, whereas circ-HAUS4 negatively correlated with t-tau; Circ-AXL positively correlated with p-tau. CONCLUSION: Our study provides an overview of circRNA expression profile in AD, and identifies that circ-AXL, circ-GPHN and circ-PCCA hold clinical implications for guiding disease management in AD patients.
OBJECTIVE: To investigate circular RNA (circRNA) expression profile via microarray, and further assess the potential of candidate circRNAs as biomarkers in Alzheimer's disease (AD). METHODS: CircRNA expression profile in cerebrospinal fluid from 8 ADpatients and 8 control (Ctrl) subjects was assessed by microarray. Subsequently, 10 candidate circRNAs from microarray were validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in cerebrospinal fluid from 80 ADpatients and 40 Ctrl subjects. RESULTS: By microarray, 112 circRNAs were upregulated and 51 circRNAs were downregulated in ADpatients compared with Ctrl subjects, and these circRNAs were enriched in AD related pathways such as neurotrophin signaling pathway, natural killer cell mediated cytotoxicity and cholinergic synapse. By RT-qPCR, circ-LPAR1, circ-AXL and circ-GPHN were increased, whereas circ-PCCA, circ-HAUS4, circ-KIF18B and circ-TTC39C were decreased in ADpatients compared with Ctrl subjects, and these circRNAs were disclosed to predict AD risk by receiver operating characteristics curve analysis. Further forward-stepwise multivariate logistic regression revealed that circ-AXL, circ-GPHN, circ-ITPR3, circ-PCCA and cic-TTC39C were independent predictive factors for AD risk. Besides, in ADpatients, circ-AXL and circ-GPHN negatively correlated, while circ-PCCA and circ-HAUS4 positively correlated with mini-mental state examination score; Circ-AXL negatively correlated, while circ-PCCA, circ-HAUS4 and circ-KIF18B positively correlated with Aβ42; Circ-AXL and circ-GPHN positively correlated, whereas circ-HAUS4 negatively correlated with t-tau; Circ-AXL positively correlated with p-tau. CONCLUSION: Our study provides an overview of circRNA expression profile in AD, and identifies that circ-AXL, circ-GPHN and circ-PCCA hold clinical implications for guiding disease management in ADpatients.