# Exploration Novel Therapeutic Targets for Periodontitis via Stress Granules Biomarkers

## TL;DR

This study identifies four stress granule-related biomarkers in periodontitis and suggests potential drugs, offering new insights into the disease's causes and treatment.

## Contribution

The study introduces four novel stress granule-related biomarkers for periodontitis and proposes potential therapeutic drugs.

## Key findings

- Four SGs-related biomarkers (PECAM1, IL18, EGFR, CCL5) were identified in periodontitis.
- The biomarkers showed significant overexpression or underexpression in PD patients.
- Potential drugs like mycophenolate and afatinib were linked to the identified biomarkers.

## Abstract

Periodontitis (PD) is associated with stress granules (SGs), which are involved in cellular stress responses. Identifying biomarkers related to SGs in PD is key to grasping its pathogenesis and devising novel therapeutic approaches.

Microarray datasets GSE10334 and GSE106090 were downloaded from public databases, and experimental verification was conducted. Differentially expressed genes (DEGs) were determined via differential expression analysis and intersected with SGs-related genes (SGs-RGs). Machine learning methods and gene expression validation were used to further refine the biomarkers. Subsequently, a nomogram was constructed for disease prediction, and its accuracy was evaluated. Enrichment analysis was performed to investigate signalling pathways, and immune cell infiltration was analysed. Moreover, a transcription factor (TF) regulatory network and a disease-biomarker-drug interaction network were constructed.

A total of 1618 DEGs, 95 candidate genes, and 4 biomarkers (PECAM1, IL18, EGFR, and CCL5) were identified. PECAM1 and CCL5 showed significant overexpression, while IL18 and EGFR showed significant underexpression in PD patients. The nomogram based on these biomarkers showed high predictive accuracy. Enrichment analysis revealed that the biomarkers were primarily accumulated within translation pathways, like ribosome and rRNA processing. Besides, 14 differentially infiltrated immune cell types were identified, with significant positive correlations between IL18/EGFR and memory B cells, PECAM1/CCL5 and plasma cells. Notably, TFs such as CEBPB, ELF1, and STAT3 were identified as important regulatory factors for the biomarkers. Potential drugs for the biomarkers included mycophenolate, afatinib, and fluticasone, and the biomarkers were associated with diseases such as gingivitis, diabetes, and cardiovascular diseases.

This study identified four SGs-related biomarkers in PD and proposed potential therapeutic targets, providing preliminary insights into its pathogenesis that warrant further experimental validation at the protein and functional levels.