Comprehensive analysis of DNA methylation gene expression profiles in GEO dataset reveals biomarkers related to malignant transformation of sinonasal inverted papilloma

Abstract

Abstract Background DNA methylation may be involved in the regulation of malignant transformation from sinonasal inverted papilloma (SNIP) to squamous cell carcinoma (SCC). The study of gene methylation changes and screening of differentially methylated loci (DMLs) are helpful to predict the possible key genes in the malignant transformation of SNIP-SCC. Materials and methods Microarray dataset GSE125399 was downloaded from the Gene Expression Omnibus (GEO) database and differentially methylated loci (DMLs) were analyzed using R language (Limma package). ClusterProfiler R package was used to perform Gene Ontology (GO) analysis on up-methylated genes and draw bubble maps. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and its visualization analysis were analyzed to speculate the possible key Genes in SNIP-SCC malignant transformation. Subsequently, SNIP cases archived in our department were collected, tissue microarray was made, and immunohistochemical staining was performed to analyze the expression levels of UCKL1, GSTT1, HLA-G, MAML2 and NRGN in different grades of sinonasal papilloma tissues. Results Analysis of dataset GSE125399 identified 56 DMLs, including 49 upregulated DMLs and 7 downregulated DMLs. Thirty-one genes containing upregulated DNA methylation loci and three genes containing downregulated DNA methylation loci were obtained by methylation microarray annotation analysis. In addition, KEGG pathway visualization analysis of 31 up-methylated genes showed that there were four significantly up-methylated genes including UCKL1, GSTT1, HLA-G and MAML2, and one significantly down-methylated gene NRGN. Subsequently, compared with non-neoplasia nasal epithelial tissues, the expression of HLA-G and NRGN was upregulated in grade I, II, III and IV tissues, while the expression of MAML2 was lost. The protein expression changes of MAML2 and NRGN were significantly negatively correlated with their gene methylation levels. Conclusions By analyzing the methylation dataset, we obtained four up-regulated methylation genes UCKL1, GSTT1, HLA-G and MAML2 and one down-regulated gene NRGN. MAML2, a tumor suppressor gene with high methylation modification but loss of protein expression, and NRGN, a tumor gene with low methylation modification but upregulated protein expression, can be used as biological indicators to judge the malignant transformation of SNIP-SCC.