Bioinformatics Identification of Candidate Biomarkers Associated with Periodontitis in Cytokine-Treated Periodontal Ligament Stem Cells

Summary

The current study is aimed at exploring dysregulated genes via bioinformatics analysis in cytokine-treated periodontal ligament stem cells. Upregulated ERC2-IT1 and downregulated EPB41L4A-AS1 were identified as key molecules in these cells, which may serve as candidate biomarkers potentially associated with periodontitis.

Abstract

The primary objective of the current study was to investigate the potential molecular mechanism underlying cytokine-induced periodontal ligament stem cells (PDLSCs). Microarray data from the GSE260558 dataset were retrieved from the gene expression omnibus (GEO) database. After normalization, the dysregulated genes were identified. Then, the different expressed mRNAs (DE-mRNAs) were subjected to gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses. Subsequently, hub genes were screened out based on protein-protein interaction (PPI) network. Finally, the competitive endogenous RNA (ceRNA) regulatory networks containing hub genes were established by using ENCORI website. A total of 739 different expressed lncRNAs (DE-lncRNAs) and 809 different expressed mRNAs (DE-mRNAs) were identified by using the limma package in the R programming language. Then, the GO and KEGG analyses demonstrated that the DE-mRNAs were closely associated with inflammation and extracellular matrix formation in cytokine-treated PDLSCs. A protein-protein interaction (PPI) network was constructed for DE-mRNAs, and hub genes were obtained by evaluating the degree. A total of 12 upregulated hub genes and 12 downregulated hub genes were screened. These upregulated and downregulated hub genes were then used to construct a ceRNA regulatory network, respectively. In these networks, upregulated ERC2-IT1 and downregulated EPB41L4A-AS1 were considered as key lncRNAs in periodontitis. In conclusion, the bioinformatics analysis revealed several key genes and a ceRNA regulatory network that participated in the progression of cytokine-induced PDLSCs. Although this study identified several candidate biomarkers related to periodontitis, further experimental validation in animal models or clinical samples is necessary.

Introduction

According to the World Health Organization, periodontitis is recognized as the sixth most prevalent, ubiquitous, and irreversible chronic inflammatory disease among humans^1,2. In the present era, it affects nearly 15% of the global population^1,2,3. It has been reported that periodontitis is strongly correlated with a variety of factors such as age, gender, oral hygiene practices, smoking status, ethnicity, lower socioeconomic status, and a range of systemic diseases^2,

Protocol

1. Dataset

This analysis used publicly available data and did not involve human or animal subjects, in compliance with institutional and JoVE ethical guidelines. All the information related to the used platforms is available in the Table of Materials.

1. Select a total of 6 samples (GSM8119437, GSM8119438, GSM8119439, GSM8119440, GSM8119441, and GSM8119442) from the GSE260558 dataset (National Center of Biotechnology Information Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE260558) for bioinformatics analysis.
2. In these samples, culture periodontal ligament....

Results

Identification of DEGs
The limma R software was used to analyze the differential expression profiles of lncRNAs and mRNAs. The differences between the two groups were determined by a non-paired t-test. As depicted in the volcano diagram (Figure 2), a total of 739 different expressed lncRNAs (DE-lncRNAs, p value<0.05, |log2FC|>1) were identified. Among these DE-lncRNAs, 340 upregulated lncRNAs and 399 downregulated lncRNAs were identified in the cytokines g.......

Discussion

Periodontal ligament stem cells (PDLSCs) are regarded as key cells that play a crucial role in suppressing periodontal damage throughout both the progression and recovery stages of periodontitis^32,33. A considerable amount of evidence has clearly demonstrated that incubation under an inflammatory condition has the potential to significantly accelerate the senescence of PDLSCs^34,35. However, it is worth no.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
R	R Foundation for Statistical Computing	4.0.4	Core open-source environment for statistical computing. Serves as the foundation for all subsequent analyses. Typically used with RStudio.
Limma	Bioconductor Community (Lead Dev: Walter & Eliza Hall Institute)	3.46.0	Specialized package for differential expression analysis of microarrays and RNA-seq.
Cytoscape	Cytoscape Consortium	3.8.2	Open-source platform for network visualization. Requires separate installation; supports plugins like ClueGO and cytoHubba via App Store.
STRING	European Molecular Biology Laboratory	12	The world's largest database for predicted and known protein-protein interactions (PPI). Accessed via web or Cytoscape plugin; provides confidence scores.
ENCORI	Sun Yat-sen University	3	Public database focusing on ceRNA networks (miRNA-mRNA-lncRNA).