Literature DB >> 27913255

Using the BITOLA system to identify candidate molecules in the interaction between oral lichen planus and depression.

Yuanbo Zhan1, Shuang Zhou1, Ying Li1, Sen Mu1, Ruijie Zhang2, Xuejing Song3, Feng Lin1, Ruimin Zhang4, Bin Zhang5.   

Abstract

Exacerbations of oral lichen planus (OLP) have been linked to the periods of psychological stress, anxiety and depression. The specific mechanism of the interaction is unclear. The aim of this study was to explore the candidate genes or molecules that play important roles in the interaction between OLP and depression. The BITOLA system was used to search all intermediate concepts relevant to the "Gene or Gene Product" for OLP and depression, and the gene expression data and tissue-specific gene data along with manual checking were then employed to filter the intermediate concepts. Finally, two genes (NCAM1, neural cell adhesion molecule 1; CD4, CD4 molecule) passed the follow-up inspection. By using the text mining can formulate a new hypothesis: NCAM1 and CD4 were identified as involved or potentially involved in the interaction between OLP and depression. These results offer a new clue for the experimenters and hold promise for developing innovative therapeutic strategies for these two diseases.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Gene expression profiling; Mental disorders; Mouth diseases; Text mining

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Year:  2016        PMID: 27913255     DOI: 10.1016/j.bbr.2016.11.047

Source DB:  PubMed          Journal:  Behav Brain Res        ISSN: 0166-4328            Impact factor:   3.332


  2 in total

1.  Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression.

Authors:  Zhenguo Dai; Qian Li; Guang Yang; Yini Wang; Yang Liu; Zhilei Zheng; Yingfeng Tu; Shuang Yang; Bo Yu
Journal:  BMC Med Genet       Date:  2019-06-11       Impact factor: 2.103

2.  Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining.

Authors:  Yichong Zhang; Yuanbo Zhan; Yuhui Kou; Xiaofeng Yin; Yanhua Wang; Dianying Zhang
Journal:  PeerJ       Date:  2020-01-03       Impact factor: 2.984

  2 in total

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