Application of Principle Component Analysis and Logistic Regression in Analyzing miRNA Markers of Brain Arteriovenous Malformation

• Published in: Shanghai jiao tong da xue xue bao Vol. 19; no. 6; pp. 641 - 645
• Main Author: 蒋路 黄俊 张志君 杨国源 王永亭
• Format: Journal Article
• Language: English
• Published: Heidelberg Shanghai Jiaotong University Press 01.12.2014
• Subjects: Architecture; Brain; Computer Science; Electrical Engineering; Engineering; Life Sciences; Logistics; Magnetic resonance imaging; Markers; Materials Science; Mathematical models; Regression; Regression analysis; Ribonucleic acids; Risk; brain arteriovenous malformation (BAVM); logistic regression (LR); principle component analysis (PCA); microRNAs (miRNAs); Q 189
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-014-1560-0

Brain arteriovenous malformation（BAVM） is frequently described as vascular malformation. Although computer tomography（CT）, magnetic resonance imaging（MRI） and angiography can clearly detect lesions, there are no diagnostic biological markers of BAVM available. Current study demonstrated that micro RNA（mi RNA）showed a feasible marker for vascular disease. To find key correlations between these mi RNAs and the onset of BAVM, we carried out chip analysis of serum mi RNAs by identifying 18 potential markers of BAVM. We then constructed a principle component analysis and logistic regression（PCA-LR） model to analyze the 18 mi RNAs collected from 77 patients. Another 9 independent samples were used to test the resulting model. The results showed that mi RNAs hsa-mir-126-3p and hsa-mir-140 are important protective factors, while hsa-mir-338 is a dominating risk factor, all of which have stronger correlation with BAVM than others. We also compared the testing results using PCA-LR model with those using LR model. The comparison revealed that PCA-LR model is better in predicting the disease.