Identification of six candidate genes for endometrial carcinoma by bioinformatics analysis

Abstract

Abstract Background Endometrial carcinoma (EC) is the most common gynecological malignant tumors which poses a serious threat to women health. This study aimed to screen the candidate genes differentially expressed in EC by bioinformatics analysis. Methods GEO database and GEO2R online tool were applied to screen the differentially expressed genes (DEGs) of EC from the microarray datasets. Protein-protein interaction (PPI) network for the DEGs was constructed to further explore the relationships among these genes and identify hub DEGs. Gene ontology and KEGG enrichment analyses were performed to investigate the biological role of DEGs. Besides, correlation analysis, genetic alteration, expression profile, and survival analysis of these hub DEGs were also investigated to further explore the roles of these hub gene in mechanism of EC tumorigenesis. qRT-PCR analysis was also performed to verify the expression of identified hub DEGs. Results A total of 40 DEGs were screened out as the DEGs with 3 upregulated and 37 downregulated in EC. The gene ontology analysis showed that these genes were significantly enriched in cell adhesion, response to estradiol, and growth factor activity, etc. The KEGG pathway analysis showed that DEGs were enriched in focal adhesion, leukocyte transendothelial migration, PI3K-Akt signaling pathway, and ECM-receptor interaction pathway. More importantly, COL1A1, IGF1, COL5A1, CXCL12, PTEN, and SPP1 were identified as the hub genes of EC. The genetic alteration analysis showed that hub genes were mainly altered in mutation and deep deletion. Expression validation by bioinformatic analysis and qRT-PCR also proved the expression of these six hub genes were differentially expressed in EC. Additionally, significantly better overall survival and disease-free survival were observed with six hub genes altered, and survival outcome in high expression of COL1A1, IGF1, and PTEN patients was also significantly better than low expression patients. Conclusions COL1A1, IGF1, COL5A1, CXCL12, PTEN, and SPP1 involved in the pathogenesis of EC and might be candidate genes for diagnosis of EC.