Single-cell RNA-seq combined with bulk RNA-seq analysis identifies necroptosis-related genes as therapeutic targets for periodontitis.

Background: Necroptosis, a regulated form of programmed cell death, exacerbates inflammatory responses by releasing damage-associated molecular patterns and inflammatory factors. However, the specific mechanisms underlying necroptosis in periodontitis remain largely unclear. This study integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to identify core necroptosis-related genes (NRGs) and validated these findings using external datasets and periodontitis samples collected during our research.

Methods: Overlapping genes were identified through a comparative analysis of 114 NRGs sourced from GeneCards and marker genes specific to various cell types in the single-cell GSE171213 periodontitis dataset. Based on these genes, cells were categorized into high- and low-necroptosis score groups. Key NRGs were identified through intersection analysis of differentially expressed genes in the high necroptosis group using the GSE10334 bulk RNA-seq dataset, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG)/ Gene Ontology (GO) enrichment analysis. Machine learning further identified hub genes associated with the inflammatory response in periodontitis. Consensus clustering analysis, clinical diagnostic model construction, gene set variation analysis, and gene set enrichment analysis were performed based on these hub genes. The model's predictive performance was validated using independent datasets and periodontitis tissue samples.

Results: We identified 10 cell types in periodontitis tissues and observed changes in the abundance of various cell populations in affected samples. Furthermore, we selected 35 NRGs differentially expressed in specific cell populations, with neutrophils and macrophages showing higher necroptosis scores. By integrating bulk RNA-seq data, we further identified 29 key NRGs. KEGG/GO analysis indicated their enrichment in inflammatory response signaling pathways. Machine learning highlighted six hub genes (CSF3R, CSF2RB, BTG2, CXCR4, GPSM3, and SSR4), all of which were highly expressed in periodontitis tissues. Consensus clustering based on these genes divided patients with periodontitis into two subgroups with distinct expression profiles. The clinical diagnostic model constructed based on these six key genes exhibited excellent diagnostic performance. Both external independent validation sets and clinical sample tests confirmed high expression of these six key genes in periodontitis tissues.

Conclusion: Our study identified six hub genes (CSF3R, CSF2RB, BTG2, CXCR4, GPSM3, and SSR4) highly expressed in periodontitis tissues and positively correlated with necroptosis. These genes may serve as therapeutic targets for inflammatory diseases like periodontitis.