STAT1: a novel candidate biomarker and potential therapeutic target of the recurrent aphthous stomatitis

Mingchen Cao,Lei Li,Longyuan Wang,Long Xu,Changkai Zhou,Wei Ren,Fanbo Jing,Xiaomin Xing,Mengxiang Fang

doi:10.1186/s12903-021-01776-w

Abstract

BackgroundThe recurrent aphthous stomatitis (RAS) frequently affects patient quality of life as a result of long lasting and recurrent episodes of burning pain. However, there were temporarily few available effective medical therapies currently. Drug target identification was the first step in drug discovery, was usually finding the best interaction mode between the potential target candidates and probe small molecules. Therefore, elucidating the molecular mechanism of RAS pathogenesis and exploring the potential molecular targets of medical therapies for RAS was of vital importance.MethodsBioinformatics data mining techniques were applied to explore potential novel targets, weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression module of the gene chip data from GSE37265, and the hub genes were identified by the Molecular Complex Detection (MCODE) plugin.ResultsA total of 16 co-expression modules were identified, and 30 hub genes in the turquoise module were identified. In addition, functional analysis of Hub genes in modules of interest was performed, which indicated that such hub genes were mainly involved in pathways related to immune response, virus infection, epithelial cell, signal transduction. Two clusters (highly interconnected regions) were determined in the network, with score = 17.647 and 10, respectively, cluster 1 and cluster 2 are linked by STAT1 and ICAM1, it is speculated that STAT1 may be a primary gene of RAS. Finally, genistein, daidzein, kaempferol, resveratrol, rosmarinic acid, triptolide, quercetin and (-)-epigallocatechin-3-gallate were selected from the TCMSP database, and both of them is the STAT-1 inhibitor. The results of reverse molecular docking suggest that in addition to triptolide, (-)-Epigallocatechin-3-gallate and resveratrol, the other 5 compounds (flavonoids) with similar structures may bind to the same position of STAT1 protein with different docking score.ConclusionsOur study identified STAT1 as the potential biomarkers that might contribute to the diagnosis and potential therapeutic target of RAS, and we can also screen RAS therapeutic drugs from STAT-1 inhibitors.

Highlights

The recurrent aphthous stomatitis (RAS) frequently affects patient quality of life as a result of long lasting and recurrent episodes of burning pain
Differential expression genes (DEGs) analysis The matrix file was annotated with an official gene symbol using the data table of the microarray platform, the “sva” R package was used to conduct batch normalization of the original expression data, and a normalized gene expression matrix file containing data was obtained for DEGs analysis
In the principal component analysis (PCA) diagram, principal component 1 (PC1) and principal component 2 (PC2) are used as the X-axis and Y-axis, respectively, to draw the scatter diagram, where each point represents a sample, the farther the two samples are from each other, the greater the difference is between the two samples in gene expression patterns

Summary

Introduction

The recurrent aphthous stomatitis (RAS) frequently affects patient quality of life as a result of long lasting and recurrent episodes of burning pain. Elucidating the molecular mechanism of RAS pathogenesis and exploring the potential molecular targets of medical therapies for RAS is of vital importance. How to use bioinformatics technology to deeply explore the potential value of these data has become one of the important directions for studying the molecular mechanisms of diseases. The bioinformatics analysis methods can help us study the molecular mechanism of diseases and discover potential therapeutic targets from a systematic perspective [8, 9]. It has been successfully used to study various biological processes, proving to be quite helpful for the identification of candidate biomarkers and potential therapeutic targets [10, 11]

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Results

Conclusion