基于生物信息学分析探索种植体周围炎的免疫特征基因及其对免疫细胞的调控机制

Immunogenetic features and their regulatory mechanisms on immune cells in peri-implantitis：a bioinformatics analysis

摘要:

目的 基于生物信息学分析探究种植体周围炎（peri-implantitis）疾病发展中明显浸润的免疫细胞及关键的免疫相关基因。方法 整合美国国家生物技术信息中心基因表达数据库（gene expression omnibus，GEO）中的GSE106090、GSE33774及GSE57631数据集，通过单样本基因集富集分析（single-sample gene set enrichment analysis，ssGSEA）评估种植体周围炎及健康牙龈组织中的免疫细胞浸润分数，并利用套索算法（least absolute shrinkage and selection operator，LASSO）筛选关键的免疫基因。结果 整合3个数据集并去批次效应后，使用“ClusterProfiler”包实现种植体周围炎的基因本体论（gene ontology，GO）及京都基因与基因组百科全书（kyoto encyclopedia of genes and genomes，KEGG）数据库的富集分析，以识别在种植体周围炎中主要上调和下调的信号通路及生物学过程。本研究进一步将差异基因与ImmPort数据库中获得的免疫相关基因取交集，通过LASSO回归筛选变量后，成功鉴定关键的疾病免疫特征性基因，包括趋化因子CC配体18（C-C motif chemokine ligand 18，CCL18）、白细胞介素-1β（interleukin 1 beta，IL-1β）、补体C3（complement C3，C3）、白细胞介素-6（interleukin 6，IL6）、利钠肽受体-3（natriuretic peptide receptor 3，NPR3）、肽酶抑制因子-3（peptidase inhibitor 3，PI3）、白细胞免疫球蛋白样受体-B3（leukocyte immunoglobulin like receptor B3，LILRB3）、富亮氨酸重复序列G蛋白偶联受体-4（leucine rich repeat containing G protein-coupled receptor 4，LGR4）。随后，本研究进行与ssGSEA免疫浸润分数的相关性分析，这些基因与种植体周围软组织内明显增加的23种免疫细胞呈现出不同程度的相关性。通过GO和KEGG数据库的富集分析，发现IL1B、IL6、CCL18、C3、LGR4、PI3、LILRB3等基因主要参与体液免疫、适应性免疫、白细胞迁移以及皮肤表皮发育等生物过程，而NPR3主要与白细胞增殖和体液水平调节等生物过程相关。结论 通过运用生物信息学的方法对免疫相关差异基因进行筛选，本研究成功识别出8个关键的免疫特征性基因，参与了种植体周围炎免疫应答及炎症响应的多个环节，并对种植体周围炎疾病背景具有较高敏感性。这些免疫特征性基因的识别为深入理解种植体周围炎的发病机制以及开发新的治疗策略提供重要的分子靶标。

Abstract:

Objective To investigate the immune cells with significant infiltration and key immune-related genes in the progression of peri-implantitis based on bioinformatics analysis.Methods The GSE106090，GSE33774，and GSE57631 datasets from the NCBI Gene Expression Omnibus（GEO） were integrated. The single-sample gene set enrichment analysis（ssGSEA） was used to assess the immune cell infiltration score of peri-implantitis tissue and healthy gingival tissue，and the least absolute shrinkage and selection operator（LASSO） regression analysis was used to identify key immune genes.Results After the three datasets were integrated and the batch effect was removed，the ClusterProfiler package was used to perform gene ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） Gene Set Enrichment Analysis（GSEA） for peri-implantitis to identify significantly upregulated and downregulated signaling pathways and biological processes. The differentially expressed genes were intersected with the immune-related genes obtained from the ImmPort database，and key immune genes of the disease were successfully identified by the LASSO regression analysis，including C-C motif chemokine ligand 18（CCL18），interleukin-1β（IL1B），interleukin-6（IL6），complement C3（C3），natriuretic peptide receptor 3（NPR3），peptidase inhibitor 3（PI3），leukocyte immunoglobulin like receptor B3（LILRB3），and leucine rich repeat containing G protein-coupled receptor 4（LGR4）. Subsequently，a correlation analysis was conducted with ssGSEA immune infiltration score，and the results showed varying degrees of correlation between these genes and the 23 types of immune cells with a significant increase in peri-implant soft tissue. GO and KEGG enrichment analyses showed that the genes such as IL1B，IL6，CCL18，C3，LGR4，PI3，and LILRB3 were mainly involved in the biological processes such as humoral immunity，adaptive immunity，leukocyte migration，and skin epidermal development，while NPR3 was mainly associated with the biological processes such as leukocyte proliferation and body fluid regulation.Conclusion Differentially expressed immune-related genes are obtained by the bioinformatics method，and eight key immune genes are identified，which participate in multiple links of immune response and inflammatory response in peri-implantitis and exhibit high sensitivity to the disease background of peri-implantitis. The identification of these immune genes provides important molecular targets for a deeper understanding of the pathogenesis of peri-implantitis and the development of novel therapeutic strategies.