Immune-associated biomarkers for early diagnosis of Parkinson’s disease based on hematological IncRNA–mRNA co-expression

• Type of Material: Article
• Authors: Kecheng Lei, Emory University; Liwen Zhang, Shanghai Biochip Co., Ltd.; Yijing He, Tongji University School of Medicine; Hui Sun, Tongji University School of Medicine; Weifang Tong, Tongji University School of Medicine; Yichun Xu, Emory University; Lingjing Jin, Tongji University School of Medicine
• Language: English
• Date: 2020-12-01
• Publisher: Portland Press
• Publication Version: Final Published Version
• Copyright Statement: © 2020 The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1042/BSR20202921
• Title of Journal or Parent Work: Bioscience Reports
• Volume: 40
• Issue: 12
• Grant/Funding Information: This work was supported by the ‘National Key R&D Program of China’ [grant numbers 2018YFC1314700, 2018YFA0108000]; the National Natural Science Foundation of China [grant numbers 81974196, 81873779]; the Shanghai Municipal Medical and Health Excellent Academic Leaders Training Program [grant number 2017BR029]; the China Postdoctoral Science Foundation [grant number 2018M632168]; the Shanghai Natural Science Fund [grant number 18ZR1427600].
• Keywords: Integrative genomics; Immune response; Parkinson’s disease; Biomarkers; LncRNA-mRNA coexpression network
• Research Categories: Biology, General; Health Sciences, Immunology

Abstract

Early stage diagnosis of Parkinson’s disease (PD) is challenging without significant motor symptoms. The identification of effective molecular biomarkers as a hematological indication of PD may help improve the diagnostic timelines and accuracy. In the present paper, we analyzed and compared the blood samples of PD and control (CTR) patients to identify the disease-related changes and determine the putative biomarkers for PD diagnosis. Based on the RNA sequencing analysis, differentially expressed genes (DEGs) were identified, and the co-expression network of DEGs was constructed using the weighted gene correlation network analysis (WGCNA). The analysis leads to the identification of 87 genes that were exclusively regulated in the PD group, whereas 66 genes were significantly increased and 21 genes were significantly decreased in contrast with the control group. The results indicate that the core lncRNA–mRNA co-expression network greatly changes the immune response in PD patients. Specifically, the results showed that Prader Willi Angelman Region RNA6 (PWAR6), LINC00861, AC83843.1, IRF family, IFIT family and calcium/calmodulin-dependent protein kinase IV (CaMK4) may play important roles in the immune system of PD. Based on the findings from the present study, future research aims at identifying novel therapeutic strategies for PD.