Research Papers: Pathology:
Towards a multi protein and mRNA expression of biological predictive and distinguish model for post stroke depression
Metrics: PDF 2217 views | HTML 2789 views | ?
Yingying Yue1,2, Haitang Jiang1,2, Rui Liu3, Yingying Yin1,2, Yuqun Zhang1,2, Jinfeng Liang1,2, Shenghua Li4, Jun Wang5, Jianxin Lu6, Deqin Geng7, Aiqin Wu8 and Yonggui Yuan1,2
1 Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, PR China
2 Institute of Psychosomatics, Medical School of Southeast University, Nanjing, PR China
3 School of Information Science and Engineering Southeast University, Nanjing, PR China
4 Department of Neurology, Jiangning Nanjing Hospital, Nanjing, PR China
5 Department of Neurology, The Affiliated First Hospital of Nanjing Medical University, Nanjing, PR China
6 Department of Neurology, The Peoples’ Hospital of Gaochun County, Nanjing, PR China
7 Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Xuzhou, PR China
8 Department of Psychosomatics, The Affiliated First Hospital of Suzhou University, Suzhou, PR China
Yonggui Yuan, email:
Keywords: post stroke depression; neurotrophic factors; protein; mRNA; Pathology Section
Received: April 18, 2016 Accepted: July 19, 2016 Published: August 05, 2016
Previous studies suggest that neurotrophic factors participate in the development of stroke and depression. So we investigated the utility of these biomarkers as predictive and distinguish model for post stroke depression (PSD). 159 individuals including PSD, stroke without depression (Non-PSD), major depressive disorder (MDD) and normal control groups were recruited and examined the protein and mRNA expression levels of vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptors (VEGFR2), placental growth factor (PIGF), insulin-like growth factor (IGF-1) and insulin-like growth factor receptors (IGF-1R). The chi-square test was used to evaluate categorical variable, while nonparametric test and one-way analysis of variance were applied to continuous variables of general characteristics, clinical and biological changes. In order to explore the predictive and distinguish role of these factors in PSD, discriminant analysis and receiver operating characteristic curve were calculated. The four groups had statistical differences in these neurotrophic factors (all P < 0.05) except VEGF concentration and IGF-1R mRNA (P = 0.776, P = 0.102 respectively). We identified these mRNA expression and protein analytes with general predictive performance for PSD and Non-PSD groups [area under the curve (AUC): 0.805, 95% CI, 0.704-0.907, P < 0.001]. Importantly, there is an excellent predictive performance (AUC: 0.984, 95% CI, 0.964-1.000, P < 0.001) to differentiate PSD patients from MDD patients. This was the first study to explore the changes of neurotrophic factors family in PSD patients, the results intriguingly demonstrated that the combination of protein and mRNA expression of biological factors could use as a predictive and discriminant model for PSD.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.