Jing Han1, Gang-Hua Feng2, Hua-Wu Liu1, Ji-Ping Yi2, Ji-Bao Wu2, Xiao-Xi Yao2. 1. School of Basic Medical Sciences, Xiangnan University Chenzhou 423000, Hunan, China. 2. Department of Neurology, Chenzhou First People's Hospital Chenzhou 423000, Hunan, China.
Abstract
BACKGROUND: Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two neurodegenerative diseases. Most patients with MCI will develop AD. Early detection of AD and MCI is a crucial issue in terms of secondary prevention. Therefore, more diagnostic models need to be developed to distinguish AD patients from MCI patients. METHODS: In our research, the expression matrix and were screened from Gene Expression Omnibus (GEO) databases. A 14-gene diagnostic model was constructed with lasso logistic analysis. The efficiency and accuracy of diagnostic model have also been validated. In order to clarify the expression differences of 14 genes in health donor, AD and MCI, the blood samples of patients and healthy individuals were collected. The mRNA expression of the 14 genes in blood sample were detected. The SH-SY5Y cell injury model was constructed and biological function of POU2AF1 and ANKRD22 in SH-SY5Y have been proved. RESULTS: We obtained 16 genes which have an area under curve (AUC) ≥0.6. After that, a diagnostic model based on 14 genes was constructed. Validation in independent cohort showed that the diagnostic model has a good diagnostic efficiency. The expressions of 6 genes in AD patients were significantly lower than those in healthy individuals and MCI patients, while the expressions of 8 genes in AD patients were significantly higher than those in healthy individuals and MCI patients. In in vitro experiments, we found that two key genes POU2AF1 and ANKRD22 could regulate neuronal development by regulating cell viability and IL-6 expression. CONCLUSION: The diagnostic model established in this study has a good diagnose efficiency. Most of these genes in diagnostic model also showed diagnostic value in AD patients. This research also can help doctors make better diagnosis for the treatment and prevention of AD. AJTR
BACKGROUND: Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two neurodegenerative diseases. Most patients with MCI will develop AD. Early detection of AD and MCI is a crucial issue in terms of secondary prevention. Therefore, more diagnostic models need to be developed to distinguish AD patients from MCI patients. METHODS: In our research, the expression matrix and were screened from Gene Expression Omnibus (GEO) databases. A 14-gene diagnostic model was constructed with lasso logistic analysis. The efficiency and accuracy of diagnostic model have also been validated. In order to clarify the expression differences of 14 genes in health donor, AD and MCI, the blood samples of patients and healthy individuals were collected. The mRNA expression of the 14 genes in blood sample were detected. The SH-SY5Y cell injury model was constructed and biological function of POU2AF1 and ANKRD22 in SH-SY5Y have been proved. RESULTS: We obtained 16 genes which have an area under curve (AUC) ≥0.6. After that, a diagnostic model based on 14 genes was constructed. Validation in independent cohort showed that the diagnostic model has a good diagnostic efficiency. The expressions of 6 genes in AD patients were significantly lower than those in healthy individuals and MCI patients, while the expressions of 8 genes in AD patients were significantly higher than those in healthy individuals and MCI patients. In in vitro experiments, we found that two key genes POU2AF1 and ANKRD22 could regulate neuronal development by regulating cell viability and IL-6 expression. CONCLUSION: The diagnostic model established in this study has a good diagnose efficiency. Most of these genes in diagnostic model also showed diagnostic value in AD patients. This research also can help doctors make better diagnosis for the treatment and prevention of AD. AJTR
Authors: Sandhya Subramanian; Patricia Ayala; Teri L Wadsworth; Christopher J Harris; Arthur A Vandenbark; Joseph F Quinn; Halina Offner Journal: J Alzheimers Dis Date: 2010 Impact factor: 4.472
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