IntroductionPeriodontitis is a chronic inflammatory disease that can result in irreversible damage to the periodontal supporting tissues1. Typical clinical manifestations of periodontitis include gingival inflammation and bleeding, formation of periodontal pockets, loss of alveolar bone, and eventual tooth mobility leading to potential tooth loss if left untreated. Research has indicated that periodontitis is one of the most prevalent oral diseases worldwide, with approximately 11% of the global population suffering from severe forms2. Findings from the Fourth National Oral Health Survey revealed a prevalence rate of 52.8%, 69.3%, and 64.6% among individuals aged 35–44, 55–64, and 65–74 years old, respectively3.Dental plaque biofilm serves as the initiating factor for periodontitis. The interaction between multiple pathogens and the dysbiosis of periodontal microecology in susceptible individuals contributes to the development of periodontitis. Stimulated by subgingival plaque, immune cells infiltrate extensively into the gingival connective tissue and secrete a plethora of cytokines. On the one hand, these cytokines play a crucial role in recruiting specific immune cells, controlling pathogenic microorganisms, and modulating osteoclast activity4. For instance, proinflammatory cytokines like IL-1β and chemokine CXCL-6 exert significant regulatory effects on the inflammatory process and immune cell response associated with periodontal bone destruction5. On the other hand, they can also contribute to periodontal tissue damage. Prostaglandin E2 is another cytokine that has been implicated in periodontal hard tissue destruction and resorption6. Furthermore, there exists an association between chronic periodontitis and several systemic diseases. Literature reports have demonstrated a clear bidirectional relationship between periodontitis and type II diabetes; thus managing both conditions may help prevent their mutual occurrence7. Therefore, investigating the inflammatory regulatory mechanisms involved in the progression of periodontitis and identifying related biomarkers can not only aid in its prevention and treatment but also contribute to mitigating the development of other diseases.In recent years, the pathological changes of organelles in periodontal tissues have received widespread attention from the academic community. The endoplasmic reticulum (ER) is a crucial organelle involved in the synthesis, folding, and modification of membrane and secretory proteins. It also plays a role in forming connecting regions with other organelles. The dynamic remodeling of the ER is essential for maintaining intracellular homeostasis and preventing disease occurrence. When the protein folding load exceeds the capacity of the ER, ER stress (ERS) occurs, which is associated with the pathogenesis of various chronic inflammatory conditions8. Studies have shown that in the state of chronic periodontal inflammation, the expression of various inflammatory factors in periodontal tissues is upregulated, which can induce ERS. This in turn triggers selective autophagy of the ER, degrading dysfunctional ER membranes and restoring ER homeostasis9. Research indicates that ER-phagy, by regulating immune cells and controlling the release of inflammatory substances, plays a role in numerous processes such as multicellular organism development, differentiation, inflammation, and immune defense10. Additionally, it is closely related to the occurrence of cancer and neurodegenerative diseases11. However, research on the relationship between periodontitis and ER-phagy is currently limited, and the mechanisms of their interaction remain unclear.With the continuous advancement of bioinformatics, the use of large amounts of raw data from databases and various algorithms has enabled the exploration of relationships between genes as well as between genes and diseases. Therefore, exploring the relationship between ER-phagy and the microenvironment of periodontitis at the molecular level could be of significant importance in improving inflammation and even alveolar bone remodeling in periodontitis patients. This study utilizes bioinformatics methods to screen ER-phagy-related biomarkers of periodontitis from a vast number of genes, providing valuable insights for disease diagnosis, progression, prognosis, and personalized treatment.Materials and methodsData download and processingThe Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) is a public database used to store microarray gene expression data and other forms of high-throughput functional genomics data12. Gene expression profile datasets containing samples from healthy periodontal tissues and periodontitis tissues were retrieved from GEO. Two datasets, GSE16134 and GSE10334, were used as the training and validation sets, respectively.The training set GSE16134 included 239 patient samples and 69 control samples, and the validation set GSE10334 included 183 patient samples and 64 control samples. The raw data of both microarray datasets were based on the GPL570 platform, and probe IDs were converted to gene symbols according to the annotation information of the platform. The R package “Linear Models for Microarray Data”(“limma”) was used for data correction. ER-phagy-related genes (ERGs) were retrieved from the GeneCards database (https://www.genecards.org). Genes with a relevance score ≥ 2 were extracted for this study.Differential expression analysisThe GSE16134 dataset was used as the training set. The “limma” software package was employed for the analysis of differentially-expressed genes (DEGs). Genes were considered differentially expressed if they exhibited P 0.5. Visualization of the results pertaining to the DEGs was carried out utilizing the “ggplots” and “pheatmap” packages.Weight gene correlation network analysis (WGCNA)WGCNA, used to describe the association pattern between genes in microarray samples, can be used to find modules of highly correlated genes13. Firstly, hierarchical clustering was employed to observe sample aggregation. Once the clustering effect was deemed appropriate, scale independence and average connectivity analysis were conducted to determine the suitable soft threshold. Subsequently, the selected soft threshold was utilized to construct a co-expression network, topological overlap matrix, and gene clustering tree. Genes exhibiting similar expression patterns were assigned to corresponding modules. The expression of ERGs in each sample from the training set GSE16134 was extracted, with the module most strongly associated with periodontitis being defined as the core module. This approach aimed to further investigate the biological relationship between genes and phenotypes within this specific module. Genes in this core module were considered key genes related to periodontitis and ER-phagy (WGCNA-ERGs) for further analysis. Venn diagrams were used to identify the intersecting genes between DEGs and WGCNA-ERGs, which were then used for subsequent analysis.Gene set variation analysis (GSVA) was performed to estimate the active variation of a predetermined gene set in the samples. The ssGSEA algorithm of R package “GSVA” was used to calculate the scores of ER-phagy genes in all samples. The Wilcoxon test was used to analyze the GSVA-ERG scores between periodontitis samples and normal control samples.Construction of a protein–protein interaction (PPI) networkThe search tool to retrieve interacting genes/proteins (STRING) (https://string-db.org/) is an interactive network for studying gene and protein interactions. We used Cytoscape (https://cytoscape.org/) to build the PPI network for visual representation and additional experimental testing. Cytoscape is a visualization platform that combines biomolecular interaction networks, high-throughput expression data, and other molecular states into a conceptual framework14. Furthermore, the Molecular Complex Detection (MCODE) (http://apps.cytoscape.org/apps/mcode) plugin Cytoscape was used with a plugin with default parameters to discriminate the modules that best reflect key gene clusters. Genes from cluster 1 were selected for further analysis. Key genes (GSVA-ERGs) were combined with DEGs to identify ER-phagy genes that were highly relevant to periodontitis.Enrichment analysisTo understand the functions of genes and identify pathways with important functions, we conducted enrichment analysis, encompassing the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) analyses15,16. GO enrichment analysis was used to analyze the enrichment of DEGs in different biological process (BP), cellular component (CC) and molecular function (MF) categories. KEGG enrichment analysis was used to achieve effective clustering of DE-ERGs in signaling pathways17. Enrichment results were filtered using thresholds of P-value