Expression Profiles Reveal Involvement of , , , and in Vulvar Carcinogenesis

Abstract

Objective: The objective of this study was to identify key genes and shed light on the underlying molecular mechanisms of vulvar squamous cell carcinoma (VSCC). Methods: Bioinformatic software was utilized for the identification and characterization of key differentially expressed genes (DEGs) from microarrays GSE63678 and GSE38228, which contain VSCC and normal vulvar tissue data. These microarrays were obtained from Gene Expression Omnibus (GEO). Immunohistochemical assays (55 VSCC and 50 normal vulvar tissues) were utilized to validate the expression of VEGF , IGF1 , BIRC5 , and MMP1 screened from the identified DEGs. SPSS 18.0 software was used for statistical analyses of the relationships between IGF1 , BIRC5 , VEGF , MMP1 expression levels and patient clinicopathological characteristics. Results: A total of 141 DEGs were identified, among which 18 genes were closely correlated with the biological characteristics of VSCC. Four of the 18 genes ( VEGF , IGF1 , BIRC5 , and MMP1 ) screened from the GEO database were markedly enriched in pathways in cancer ( P < 0.05), and could be considered key genes in VSCC based on KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis in DAVID (Database for Annotation, Visualization and Integrated Discovery).The expression levels of these 4 hub genes, determined by immunohistochemical assays, were consistent with the bioinformatics results. Higher expression of IGF1 showed significant association with well-differentiated carcinomas ( P = 0.017). BIRC5 expression levels showed a positive correlation with clinical stage ( P = 0.039); compared with those in menopause for over 10 years, patients in menopause for less than 10 years at the time of diagnosis tended to have significantly higher expression of BIRC5 ( P = 0.003). VEGF and MMP1 expression levels were not correlated with any of the tested clinicopathological characteristics. Conclusion: VEGF , IGF1 , BIRC5 , and MMP1 were identified as being associated with VSCC using integrated bioinformatic methods, which may provide important insights into the pathogenesis of this disease and help to identify new biomarkers.