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. 2021 Apr 14:2021:6674744.
doi: 10.1155/2021/6674744. eCollection 2021.

Text Mining-Based Drug Discovery in Osteoarthritis

Rong-Guo Yu  1 , Jia-Yu Zhang  2 , Zhen-Tao Liu  3 , You-Guang Zhuo  1 , Hai-Yang Wang  1 , Jie Ye  1 , Nannan Liu  1 , Yi-Yuan Zhang  1
Affiliations

Text Mining-Based Drug Discovery in Osteoarthritis

Rong-Guo Yu et al. J Healthc Eng. .

Abstract

Background: Osteoarthritis (OA) is a chronic and degenerative joint disease, which causes stiffness, pain, and decreased function. At the early stage of OA, nonsteroidal anti-inflammatory drugs (NSAIDs) are considered the first-line treatment. However, the efficacy and utility of available drug therapies are limited. We aim to use bioinformatics to identify potential genes and drugs associated with OA.

Methods: The genes related to OA and NSAIDs therapy were determined by text mining. Then, the common genes were performed for GO, KEGG pathway analysis, and protein-protein interaction (PPI) network analysis. Using the MCODE plugin-obtained hub genes, the expression levels of hub genes were verified using quantitative real-time polymerase chain reaction (qRT-PCR). The confirmed genes were queried in the Drug Gene Interaction Database to determine potential genes and drugs.

Results: The qRT-PCR result showed that the expression level of 15 genes was significantly increased in OA samples. Finally, eight potential genes were targetable to a total of 53 drugs, twenty-one of which have been employed to treat OA and 32 drugs have not yet been used in OA.

Conclusions: The 15 genes (including PTGS2, NLRP3, MMP9, IL1RN, CCL2, TNF, IL10, CD40, IL6, NGF, TP53, RELA, BCL2L1, VEGFA, and NOTCH1) and 32 drugs, which have not been used in OA but approved by the FDA for other diseases, could be potential genes and drugs, respectively, to improve OA treatment. Additionally, those methods provided tremendous opportunities to facilitate drug repositioning efforts and study novel target pharmacology in the pharmaceutical industry.

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Conflict of interest statement

The authors declare no conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
Overall data mining strategy. Text mining was used to identify genes associated with the concepts of OA and NSAID using pubmed2ensemble. Extracted genes were then analyzed for their function and gene ontology using Funrich. Further enrichment was obtained by molecular network analysis using STRING and Cytoscape. The final enriched gene list was then used to determine interactions with known drugs using the Drug Gene Interaction Database.
Figure 2
Figure 2
A Venn diagram showing the overlapping genes between OA and NSAIDs.
Figure 3
Figure 3
The cellular-component annotations of those gene sets.
Figure 4
Figure 4
The molecular functions annotations of those gene sets.
Figure 5
Figure 5
The biological process of those gene sets.
Figure 6
Figure 6
The enriched biological KEGG pathway.
Figure 7
Figure 7
Protein-protein interaction (PPI) network of common genes.
Figure 8
Figure 8
The tightest module from the PPI network.
Figure 9
Figure 9
RT-PCR validation of the hub gene between OA and normal controls.
Figure 10
Figure 10
The interrelation of 32 drugs with genes and pathways.
See this image and copyright information in PMC

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