Integrative analysis of ferroptosis-related genes and immune infiltration in peri-implantitis

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Abstract
Background Peri-implantitis (PI） is a common complication of dental implants that can result in implant failure. The study aims to identify genes related to PI and Ferroptosis, analyze relevant molecules in related pathways, and perform immune infiltration analysis, with the goal of investigating the classification of PI and creating a diagnostic model using its common genes. Methods The study first identified Ferroptosis-related genes (FRGs) by downloading datasets GSE33774 and GSE57631 and intersecting them with the FRGs set. Enrichment analysis, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), was conducted to compare enrichment pathways in the Control team and the PI team. Weighted Gene Co-Expression Network Analysis (WGCNA) was then used to search for genes and build logistic regression models, Support Vector Machine (SVM) models, and Least Absolute Shrinkage and Selection Operator (LASSO) regression models. A diagnostic model of PI composed of three genes HLF, PLIN2, and MAP1LC3A was obtained. The study utilized periodontal samples from patients with normal periodontal health and those with peri-implantitis to identify significant differences in the gene MAP1LC3A between the normal team and the PI team, further verifying this clinical correlation of FRHGs. Results The calibration curves of the diagnostic model showed close results, indicating better and more accurate performance. Immune cells exhibited a strong negative connection with one of the FRHGs——MAP1LC3A in both highly and low-risk categories of PI. Additionally, the FRHGs PLIN2 and HLF had strong positive and negative correlations with immune cells called monocytes, respectively. Two categories of PI, Cluster1 and Cluster2, were determined by the transcript levels of FRHGs in PI samples from the GEO Combined Datasets. Conclusion This study identified FRGs linked to PI and established a diagnostic model (HLF, PLIN2, MAP1LC3A). The accurate model revealed immunological characteristics of PI through immune infiltration analysis. Transcription levels of FRHGs classified PI samples into Cluster1 and Cluster2, enabling personalized treatment plans. Significant transcription difference of MAP1LC3A gene between normal and PI groups suggests its potential as a diagnostic biomarker. Integrating immune infiltration analysis offers new insights for PI research, enhancing understanding of its molecular mechanisms and targeted interventions.

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