Identification of Cross-Talk and Pyroptosis-Related Genes Linking Periodontitis and Rheumatoid Arthritis Revealed by Transcriptomic Analysis

Abstract

Background. The current study is aimed at identifying the cross-talk genes between periodontitis (PD) and rheumatoid arthritis (RA), as well as the potential relationship between cross-talk genes and pyroptosis-related genes. Methods. Datasets for the PD (GSE106090, GSE10334, GSE16134) and RA (GSE55235, GSE55457, GSE77298, and GSE1919) were downloaded from the GEO database. After batch correction and normalization of datasets, differential expression analysis was performed to identify the differentially expressed genes (DEGs). The cross-talk genes linking PD and RA were obtained by overlapping the DEGs dysregulated in PD and DEGs dysregulated in RA. Genes involved in pyroptosis were summarized by reviewing literatures, and the correlation between pyroptosis genes and cross-talk genes was investigated by Pearson correlation coefficient. Furthermore, the weighted gene coexpression network analysis (WGCNA) was carried out to identify the significant modules which contained both cross-talk genes and pyroptosis genes in both PD data and RA data. Thus, the core cross-talk genes were identified from the significant modules. Receiver-operating characteristic (ROC) curve analysis was performed to identify the predictive accuracy of these core cross-talk genes in diagnosing PD and RA. Based on the core cross-talk genes, the experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed. Results. A total of 40 cross-talk genes were obtained. Most of the pyroptosis genes were not differentially expressed in disease and normal samples. By selecting the modules containing both cross-talk genes or pyroptosis genes, the blue module was identified to be significant module. Three genes, i.e., cross-talk genes (TIMP1, LGALS1) and pyroptosis gene-GPX4, existed in the blue module of PD network, while two genes (i.e., cross-talk gene-VOPP1 and pyroptosis gene-AIM2) existed in the blue module of RA network. ROC curve analysis showed that three genes (TIMP1, VOPP1, and AIM2) had better predictive accuracy in diagnosing disease compared with the other two genes (LGALS1 and GPX4). Conclusions. This study revealed shared mechanisms between RA and PD based on cross-talk and pyroptosis genes, supporting the relationship between the two diseases. Thereby, five modular genes (TIMP1, LGALS1, GPX4, VOPP1, and AIM2) could be of relevance and might serve as potential biomarkers. These findings are a basis for future research in the field.

Analysis and Experimental Validation of Rheumatoid Arthritis Innate Immunity Gene CYFIP2 and Pan-Cancer

“…Meanwhile, the emerging weighted gene co-expression network analysis (WGCNA) has gradually been used in the association between diseases along with related phenotypes and clusters (modules) of highly corrected genes ( 15 ). Several studies have illustrated the effects of the hub gene and underlying molecular mechanisms in RA patients through WGCNA analysis ( 25 , 26 ). While comprehensive immune infiltration and related pathways are still deficient.…”

Identification of Key Pyroptosis-Related Genes and Distinct Pyroptosis-Related Clusters in Periodontitis

“…Such research assumes significance considering the possibility of therapeutic interventions directed at pyroptosis blockade ( 91 , 92 ) that can offer a novel management modality for periodontitis and syndemic oral-systemic conditions. The significance of pyroptosis in the linkage of periodontitis-associated systemic disease has been recently demonstrated in the context of rheumatoid arthritis using bioinformatics analysis ( 93 ), and additional studies are warranted. The findings of this study should be addressed in the context of its limitations.…”

Bioinformatics-based approach to construct a ceRNA network between periodontitis and hereditary gingival fibroplasia

“…Through a comprehensive search of the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) by using the keyword of "hereditary gingival bromatosis(HGF)", the dataset GSE4250 (platform GPL570) was downloaded, and this dataset uses gingiva from patients with hereditary gingival bromatosis (experimental group) and normal gingiva (control group) [30]. Meanwhile, GSE10334 and GSE16134 (platform GPL570) datasets were obtained by searching in the GEO database with the keyword "periodontitis", samples in the experimental group with periodontitis, and samples in the control group are healthy gingival [31,32].…”