Your browser doesn't support javascript.
loading
Identification of repurposed drugs targeting significant long non-coding RNAs in the cross-talk between diabetes mellitus and Alzheimer's disease.
Ghiam, Shokoofeh; Eslahchi, Changiz; Shahpasand, Koorosh; Habibi-Rezaei, Mehran; Gharaghani, Sajjad.
Affiliation
  • Ghiam S; Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
  • Eslahchi C; Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. ch-eslahchi@sbu.ac.ir.
  • Shahpasand K; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. ch-eslahchi@sbu.ac.ir.
  • Habibi-Rezaei M; Department of Brain and Cognitive Sciences, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.
  • Gharaghani S; School of Biology, College of Science, University of Tehran, Tehran, Iran.
Sci Rep ; 12(1): 18332, 2022 10 31.
Article in En | MEDLINE | ID: mdl-36316461
ABSTRACT
The relationship between diabetes mellitus (DM) and Alzheimer's disease (AD) is so strong that scientists called it "brain diabetes". According to several studies, the critical factor in this relationship is brain insulin resistance. Due to the rapid global spread of both diseases, overcoming this cross-talk has a significant impact on societies. Long non-coding RNAs (lncRNAs), on the other hand, have a substantial impact on complex diseases due to their ability to influence gene expression via a variety of mechanisms. Consequently, the regulation of lncRNA expression in chronic diseases permits the development of innovative therapeutic techniques. However, developing a new drug requires considerable time and money. Recently repurposing existing drugs has gained popularity due to the use of low-risk compounds, which may result in cost and time savings. in this study, we identified drug repurposing candidates capable of controlling the expression of common lncRNAs in the cross-talk between DM and AD. We also utilized drugs that interfered with this cross-talk. To do this, high degree common lncRNAs were extracted from microRNA-lncRNA bipartite network. The drugs that interact with the specified lncRNAs were then collected from multiple data sources. These drugs, referred to as set D, were classified in to positive (D+) and negative (D-) groups based on their effects on the expression of the interacting lncRNAs. A feature selection algorithm was used to select six important features for D. Using a random forest classifier, these features were capable of classifying D+ and D- with an accuracy of 82.5%. Finally, the same six features were extracted for the most recently Food and Drug Administration (FDA) approved drugs in order to identify those with the highest likelihood of belonging to D+ or D-. The most significant FDA-approved positive drugs, chromium nicotinate and tapentadol, were presented as repurposing candidates, while cefepime and dihydro-alpha-ergocryptine were recommended as significant adverse drugs. Moreover, two natural compounds, curcumin and quercetin, were recommended to prevent this cross-talk. According to the previous studies, less attention has been paid to the role of lncRNAs in this cross-talk. Our research not only did identify important lncRNAs, but it also suggested potential repurposed drugs to control them.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: MicroRNAs / Diabetes Mellitus / Alzheimer Disease / RNA, Long Noncoding Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Sci Rep Year: 2022 Type: Article Affiliation country: Iran

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: MicroRNAs / Diabetes Mellitus / Alzheimer Disease / RNA, Long Noncoding Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Sci Rep Year: 2022 Type: Article Affiliation country: Iran