Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data. (11th December 2018)

• Record Type: Journal Article
• Authors: Wang, Bo; Zhang, Jing
• Other Names: Harrison Paul Academic Editor.
• Is Part Of: BioMed research international. Volume 2018(2018)
• Publication Date: 2018-12-11
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2018/3823082
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.